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TextAttack - My Invited Talk at UVA HMI seminar series

On April 14 2021, I gave an invited talk at the UVA Human and Machine Intelligence Seminar:

TalkSlide



General Multi-label Image Classification with Transformers

Title: General Multi-label Image Classification with Transformers

Paper ArxivVersion

GitHub: https://github.com/QData/C-Tran

Abstract

Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the uncertainty of labels during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News500, and CUB image datasets.

Citations

@article{lanchantin2020general,
      title={General Multi-label Image Classification with Transformers}, 
      author={Jack Lanchantin and Tianlu Wang and Vicente Ordonez and Yanjun Qi},
      year={2020},
      eprint={2011.14027},
      archivePrefix={arXiv, CVPR2021},
      primaryClass={cs.CV}
}

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Curriculum Labeling- Self-paced Pseudo-Labeling for Semi-Supervised Learning

Title: Curriculum Labeling- Self-paced Pseudo-Labeling for Semi-Supervised Learning”

at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) (acceptance rate: 21%))

authors: Paola Cascante-Bonilla, Fuwen Tan, Yanjun Qi, Vicente Ordonez

Paper Arxiv

Abstract

In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled samples and a large set of unlabeled samples. Pseudo-labeling works by applying pseudo-labels to samples in the unlabeled set by using a model trained on the combination of the labeled samples and any previously pseudo-labeled samples, and iteratively repeating this process in a self-training cycle. Current methods seem to have abandoned this approach in favor of consistency regularization methods that train models under a combination of different styles of self-supervised losses on the unlabeled samples and standard supervised losses on the labeled samples. We empirically demonstrate that pseudo-labeling can in fact be competitive with the state-of-the-art, while being more resilient to out-of-distribution samples in the unlabeled set. We identify two key factors that allow pseudo-labeling to achieve such remarkable results (1) applying curriculum learning principles and (2) avoiding concept drift by restarting model parameters before each self-training cycle. We obtain 94.91% accuracy on CIFAR-10 using only 4,000 labeled samples, and 68.87% top-1 accuracy on Imagenet-ILSVRC using only 10% of the labeled samples. The code is available at following https URL

code

Citations

@misc{grigsby2020measuring,
      title={Measuring Visual Generalization in Continuous Control from Pixels}, 
      author={Jake Grigsby and Yanjun Qi},
      year={2020},
      eprint={2010.06740},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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Measuring Visual Generalization in Continuous Control from Pixels

Title: Measuring Visual Generalization in Continuous Control from Pixels

  • authors: Jake Grigsby, Yanjun Qi

Paper Arxiv

Code Here

Abstract

Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents in continuous control tasks. However, it is still unclear whether current techniques can face a variety of visual conditions required by real-world environments. We propose a challenging benchmark that tests agents’ visual generalization by adding graphical variety to existing continuous control domains. Our empirical analysis shows that current methods struggle to generalize across a diverse set of visual changes, and we examine the specific factors of variation that make these tasks difficult. We find that data augmentation techniques outperform self-supervised learning approaches and that more significant image transformations provide better visual generalization \footnote{The benchmark and our augmented actor-critic implementation are open-sourced @ this https URL)

Citations

@misc{grigsby2020measuring,
      title={Measuring Visual Generalization in Continuous Control from Pixels}, 
      author={Jake Grigsby and Yanjun Qi},
      year={2020},
      eprint={2010.06740},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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Transfer Learning with Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans

Title: Transfer Learning with Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans

  • authors: Jack Lanchantin, Arshdeep Sekhon, Clint Miller, Yanjun Qi

Paper BioArxiv

Talk: Slide Coming

Abstract

The novel coronavirus SARS-CoV-2, which causes Coronavirus disease 2019 (COVID-19), is a significant threat to worldwide public health. Viruses such as SARS-CoV-2 infect the human body by forming interactions between virus proteins and human proteins that compromise normal human protein-protein interactions (PPI). Current in vivo methods to identify PPIs between a novel virus and humans are slow, costly, and difficult to cover the vast interaction space. We propose a novel deep learning architecture designed for in silico PPI prediction and a transfer learning approach to predict interactions between novel virus proteins and human proteins. We show that our approach outperforms the state-of-the-art methods significantly in predicting Virus–Human protein interactions for SARS-CoV-2, H1N1, and Ebola.

Citations

@article {Lanchantin2020.12.14.422772,
	author = {Lanchantin, Jack and Sekhon, Arshdeep and Miller, Clint and Qi, Yanjun},
	title = {Transfer Learning with MotifTransformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans},
	elocation-id = {2020.12.14.422772},
	year = {2020},
	doi = {10.1101/2020.12.14.422772},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2020/12/15/2020.12.14.422772},
	eprint = {https://www.biorxiv.org/content/early/2020/12/15/2020.12.14.422772.full.pdf},
	journal = {bioRxiv}
}

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Benchmarking Search Algorithms for Generating NLP Adversarial Examples

Title: Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples

  • Abstract: We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget. When new search methods are proposed in past work, the attack search space is often modified alongside the search method. Without ablation studies benchmarking the search algorithm change with the search space held constant, an increase in attack success rate could from an improved search method or a less restrictive search space. Additionally, many previous studies fail to properly consider the search algorithms’ run-time cost, which is essential for downstream tasks like adversarial training. Our experiments provide a reproducible benchmark of search algorithms across a variety of search spaces and query budgets to guide future research in adversarial NLP. Based on our experiments, we recommend greedy attacks with word importance ranking when under a time constraint or attacking long inputs, and either beam search or particle swarm optimization otherwise.

  • Citations:

    @misc{yoo2020searching,
        title={Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples}, 
        author={Jin Yong Yoo and John X. Morris and Eli Lifland and Yanjun Qi},
        year={2020},
        eprint={2009.06368},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
    }
    

Our Paper in EMNLP BlackBoxNLP.

Our search benchmarking result Github : https://github.com/QData/TextAttack-Search-Benchmark

Benchmarking Attack Recipes

  • As we emphasized in the above paper, we don’t recommend to directly compare Attack Recipes out of the box.

  • This is due to that attack recipes in the recent literature used different ways or thresholds in setting up their constraints. Without the constraint space held constant, an increase in attack success rate could come from an improved search or transformation method or a less restrictive search space.



On Quality of Generated Adversarial Examples and How to Set Attack Contraints

Title: Reevaluating Adversarial Examples in Natural Language

  • Paper EMNLP Findings

  • Abstract: State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model and follows some linguistic constraints. We then analyze the outputs of two state-of-the-art synonym substitution attacks. We find that their perturbations often do not preserve semantics, and 38% introduce grammatical errors. Human surveys reveal that to successfully preserve semantics, we need to significantly increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.With constraints adjusted to better preserve semantics and grammaticality, the attack success rate drops by over 70 percentage points.

Our Github on Reevaluation: Reevaluating-NLP-Adversarial-Examples Github

  • Citations
    @misc{morris2020reevaluating,
        title={Reevaluating Adversarial Examples in Natural Language}, 
        author={John X. Morris and Eli Lifland and Jack Lanchantin and Yangfeng Ji and Yanjun Qi},
        year={2020},
        eprint={2004.14174},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
    }
    

Some of our evaluation results on quality of two SOTA attack recipes

  • As we have emphasized in this paper, we recommend researchers and users to be EXTREMELY mindful on the quality of generated adversarial examples in natural language
  • We recommend the field to use human-evaluation derived thresholds for setting up constraints

Some of our evaluation results on how to set constraints to evaluate NLP model’s adversarial robustness



FastSK- Fast Sequence Analysis with Gapped String Kernels

Title: FastSK: Fast Sequence Analysis with Gapped String Kernels

Paper Bioinformatics

GitHub: https://github.com/QData/FastSK

Talk Slides

Talk video

demo1

demo1

Abstract

Gapped k-mer kernels with Support Vector Machines (gkm-SVMs) have achieved strong predictive performance on regulatory DNA sequences on modestly-sized training sets. However, existing gkm-SVM algorithms suffer from the slow kernel computation time, as they depend exponentially on the sub-sequence feature-length, number of mismatch positions, and the task’s alphabet size. In this work, we introduce a fast and scalable algorithm for calculating gapped k-mer string kernels. Our method, named FastSK, uses a simplified kernel formulation that decomposes the kernel calculation into a set of independent counting operations over the possible mismatch positions. This simplified decomposition allows us to devise a fast Monte Carlo approximation that rapidly converges. FastSK can scale to much greater feature lengths, allows us to consider more mismatches, and is performant on a variety of sequence analysis tasks. On 10 DNA transcription factor binding site (TFBS) prediction datasets, FastSK consistently matches or outperforms the state-of-the-art gkmSVM-2.0 algorithms in AUC, while achieving average speedups in kernel computation of 100 times and speedups of 800 times for large feature lengths. We further show that FastSK outperforms character-level recurrent and convolutional neural networks across all 10 TFBS tasks. We then extend FastSK to 7 English medical named entity recognition datasets and 10 protein remote homology detection datasets. FastSK consistently matches or outperforms these baselines. Our algorithm is available as a Python package and as C++ source code. (Available for download at https://github.com/Qdata/FastSK/. Install with the command make or pip install)

Citations

@article{10.1093/bioinformatics/btaa817,
    author = {Blakely, Derrick and Collins, Eamon and Singh, Ritambhara and Norton, Andrew and Lanchantin, Jack and Qi, Yanjun},
    title = "{FastSK: fast sequence analysis with gapped string kernels}",
    journal = {Bioinformatics},
    volume = {36},
    number = {Supplement_2},
    pages = {i857-i865},
    year = {2020},
    month = {12},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa817},
    url = {https://doi.org/10.1093/bioinformatics/btaa817},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_2/i857/35337038/btaa817.pdf},
}

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TextAttack- A Framework for Adversarial Attacks in Natural Language Processing

Title: TextAttack: A Framework for Adversarial Attacks in Natural Language Processing

GitHub: https://github.com/QData/TextAttack

Paper Arxiv

Abstract

TextAttack is a library for generating natural language adversarial examples to fool natural language processing (NLP) models. TextAttack builds attacks from four components: a search method, goal function, transformation, and a set of constraints. Researchers can use these components to easily assemble new attacks. Individual components can be isolated and compared for easier ablation studies. TextAttack currently supports attacks on models trained for text classification and entailment across a variety of datasets. Additionally, TextAttack’s modular design makes it easily extensible to new NLP tasks, models, and attack strategies. TextAttack code and tutorials are available at this https URL.

It is a Python framework for adversarial attacks, data augmentation, and model training in NLP. textAttack

Citations

@misc{morris2020textattack,
    title={TextAttack: A Framework for Adversarial Attacks in Natural Language Processing},
    author={John X. Morris and Eli Lifland and Jin Yong Yoo and Yanjun Qi},
    year={2020},
    eprint={2005.05909},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data

Title: Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data

Paper Bioinformatics

GitHub: https://github.com/QData/ChromeGCN

demo1

Abstract

Motivation

Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the chromatin profile. Deep neural networks have achieved state of the art performance on chromatin profile prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the chromatin profiles because modeling the 3D genome is challenging.

Results

In this work, we introduce ChromeGCN, a graph convolutional network for chromatin profile prediction by fusing both local sequence and long-range 3D genome information. By incorporating the 3D genome, we relax the independent and identically distributed assumption of local windows for a better representation of DNA. ChromeGCN explicitly incorporates known long-range interactions into the modeling, allowing us to identify and interpret those important long-range dependencies in influencing chromatin profiles. We show experimentally that by fusing sequential and 3D genome data using ChromeGCN, we get a significant improvement over the state-of-the-art deep learning methods as indicated by three metrics. Importantly, we show that ChromeGCN is particularly useful for identifying epigenetic effects in those DNA windows that have a high degree of interactions with other DNA windows.

Citations

@article{10.1093/bioinformatics/btaa793,
    author = {Lanchantin, Jack and Qi, Yanjun},
    title = "{Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data}",
    journal = {Bioinformatics},
    volume = {36},
    number = {Supplement_2},
    pages = {i659-i667},
    year = {2020},
    month = {12},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa793},
    url = {https://doi.org/10.1093/bioinformatics/btaa793},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/Supplement\_2/i659/35336695/btaa793.pdf},
}

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My tutorial talk about jointnets at UCLA computational genomics summer school 2019 for extracting connectomes from heterogeneous samples

Here is the slide of my tutorial talk at UCLA computational genomics summer school 2019.

Slides: PDF

Thanks for reading!



JointNets R package for Joint Network Estimation, Visualization, Simulation and Evaluation from Heterogeneous Samples

jointNets R package: a Suite of Fast and Scalable Tools for Learning Multiple Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowledge

JointNets R in CRAN : URL

Github Site: URL

Talk slide by Zhaoyang about the jointnet implementations:

  • URL

  • Youtube Talk by Zhaoyang about the jointnet implementations: URL

Demo GUI Run:

multisGGM

Demo Visualization of a few learned networks:

  • DIFFEE on one gene expression dataset about breast cancer

multisGGM

  • JEEK on one simulated data about samples from multiple contexts and nodes with extra spatial information

multisGGM

  • SIMULE on one word based text dataset including multiple categories

multisGGM

multisGGM

  • SIMULE on one multi-context Brain fMRI dataset

multisGGM

  • Demo downstream task using learned graphs for classification, e.g., on a two class text dataset, we get

multisGGM

  • With Zoom In/Out function

multisGGM

  • With Multiple window design, legend, title coloring schemes

multisGGM

Flow charts of the code design (functional and module level) in jointnets package

multisGGM multisGGM

Citations

@conference{wang2018jeek,
  Author = {Wang, Beilun and Sekhon, Arshdeep and Qi, Yanjun},
  Booktitle = {Proceedings of The 35th International Conference on Machine Learning (ICML)},
  Title = {A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models},
  Year = {2018}}
}

Support or Contact

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kDIFFNet - Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

Tool kDIFFNet: Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models

Paper: BioArxiv & PDF

Abstract

We focus on integrating different types of extra knowledge (other than the observed samples) for estimating the sparse structure change between two p-dimensional Gaussian Graphical Models (i.e. differential GGMs). Previous differential GGM estimators either fail to include additional knowledge or cannot scale up to a high-dimensional (large p) situation. This paper proposes a novel method KDiffNet that incorporates Additional Knowledge in identifying Differential Networks via an Elementary Estimator. We design a novel hybrid norm as a superposition of two structured norms guided by the extra edge information and the additional node group knowledge. KDiffNet is solved through a fast parallel proximal algorithm, enabling it to work in large-scale settings. KDiffNet can incorporate various combinations of existing knowledge without re-designing the optimization. Through rigorous statistical analysis we show that, while considering more evidence, KDiffNet achieves the same convergence rate as the state-of-the-art. Empirically on multiple synthetic datasets and one real-world fMRI brain data, KDiffNet significantly outperforms the cutting edge baselines concerning the prediction performance, while achieving the same level of time cost or less.

Citations

@conference{arsh19kdiffNet,
  Author = {Sekhon, Arshdeep and Wang, Beilun and Qi, Yanjun},
  Title = {Adding Extra Knowledge in Scalable Learning of
Sparse Differential Gaussian Graphical Models},
  Year = {2019}}
}

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Feature Squeezing- Invited Talk at GMU

On April 23 2019, I gave an invited talk at the ARO Invitational Workshop on Foundations of Autonomous Adaptive Cyber Systems

TalkSlide



Graph Neural Networks for Multi-Label Classification

Title: Neural Message Passing for Multi-Label Classification

Paper ArxivVersion

GitHub: https://github.com/QData/LaMP

Abstract

Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. Recurrent neural network (RNN) based encoder-decoder models have shown state-of-the-art performance for solving MLC. However, the sequential nature of modeling label dependencies through an RNN limits its ability in parallel computation, predicting dense labels, and providing interpretable results. In this paper, we propose Message Passing Encoder-Decoder (MPED) Networks, aiming to provide fast, accurate, and interpretable MLC. MPED networks model the joint prediction of labels by replacing all RNNs in the encoder-decoder architecture with message passing mechanisms and dispense with autoregressive inference entirely. The proposed models are simple, fast, accurate, interpretable, and structure-agnostic (can be used on known or unknown structured data). Experiments on seven real-world MLC datasets show the proposed models outperform autoregressive RNN models across five different metrics with a significant speedup during training and testing time.

Citations

@article{lanchantin2018neural,
  title={Neural Message Passing for Multi-Label Classification},
  author={Lanchantin, Jack and Sekhon, Arshdeep and Qi, Yanjun},
  year={2018}
}

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Feature Squeezing- Invited Webinar Talk at I3P

On December 21 @ 12noon, I gave a distinguished webinar talk in the Fall 2018 webinar series of the Institute for Information Infrastructure Protection (I3P) (@ the George Washington University and SRI International).

TalkSlide

Webinar Recording @ URL



My tutorial talk at UVA-CPHG seminar and healthDynamics workshop 2018 for Making Deep Learning Understandable for Genomics

I gave a tutorial talk at UVA-CPHG Seminar Series 2018.

Title: Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation

Short Version Slides @HealthDynamicsWorkshop:PDF



Thanks for reading!



DeepDiff- Deep-learning for predicting Differential gene expression from histone modifications

Tool DeepDIff: DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications

Paper:

GitHub

talk slides PDF

Abstract:

Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing differential gene regulation. Recent studies either failed to capture combinatorial effects on differential prediction or primarily only focused on cell type-specific analysis. In this paper, we develop a novel attention-based deep learning architecture, DeepDiff, that provides a unified and end-to-end solution to model and to interpret how dependencies among histone modifications control the differential patterns of gene regulation. DeepDiff uses a hierarchy of multiple Long short-term memory (LSTM) modules to encode the spatial structure of input signals and to model how various histone modifications cooperate automatically. We introduce and train two levels of attention jointly with the target prediction, enabling DeepDiff to attend differentially to relevant modifications and to locate important genome positions for each modification. Additionally, DeepDiff introduces a novel deep-learning based multi-task formulation to use the cell-type-specific gene expression predictions as auxiliary tasks, encouraging richer feature embeddings in our primary task of differential expression prediction. Using data from Roadmap Epigenomics Project (REMC) for ten different pairs of cell types, we show that DeepDiff significantly outperforms the state-of-the-art baselines for differential gene expression prediction. The learned attention weights are validated by observations from previous studies about how epigenetic mechanisms connect to differential gene expression. Codes and results are available at deepchrome.org

DeepDiffChrome

DeepDiffChrome

DeepDiffChrome

DeepDiffChrome

Citations

@article{ArDeepDiff18,
author = {Sekhon, Arshdeep and Singh, Ritambhara and Qi, Yanjun},
title = {DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications},
journal = {Bioinformatics},
volume = {34},
number = {17},
pages = {i891-i900},
year = {2018},
doi = {10.1093/bioinformatics/bty612},
URL = {http://dx.doi.org/10.1093/bioinformatics/bty612},
eprint = {/oup/backfile/content_public/journal/bioinformatics/34/17/10.1093_bioinformatics_bty612/2/bty612.pdf}
}

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My tutorial talk at ACM-BCB 2018 for Making Deep Learning Understandable for Genomics

Here are the slides of tutorial talk I gave at ACM-BCB 2018.

Title: Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation

Part I Slides: PDF

Part II Slides:PDF

Thanks for reading!



A Series of Tutorials We wrote to explain the JointS GM tools we built for extracting connectomes from heterogeneous samples

So far, we have released the following Tutorials:

No. Tutorial Name
1 Review I: Probability Foundations
2 Review II: Gaussian Graphical Model Basics
3 Review III: Markov Random Field and Log Linear Model
4 Review IV: A Unified Framework for M-estimaotr and Elementary Estimators
5 Review V: Sparse Gaussian Graphical Model estimators
6 Review VI: Multi-task sGGMs and optimization challenges
7 Review VII: Multi-task sGGMs estimators
8 Review VIII: Three metrics for evaluating estimators/learners
9 Reviews: Combined all Tutorials for Joint-sGGMs
10 201807-Beilun-Defense Talk
11 2018-BeilunDefense + 2017-AllJointGGTutorials

Contact

Have questions or suggestions? Feel free to ask me on Twitter or email me.

Thanks for reading!



JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models

Paper: Most updated version at HERE | Previous version: @Arxiv |

URL at 2018 ICML

TalkSlide: URL

R package: URL

GitRepo for R package: URL

install.packages("jeek")
library(jeek)
demo(jeek)

Abstract

We consider the problem of including additional knowledge in estimating sparse Gaussian graphical models (sGGMs) from aggregated samples, arising often in bioinformatics and neuroimaging applications. Previous joint sGGM estimators either fail to use existing knowledge or cannot scale-up to many tasks (large $K$) under a high-dimensional (large $p$) situation. In this paper, we propose a novel \underline{J}oint \underline{E}lementary \underline{E}stimator incorporating additional \underline{K}nowledge (JEEK) to infer multiple related sparse Gaussian Graphical models from large-scale heterogeneous data. Using domain knowledge as weights, we design a novel hybrid norm as the minimization objective to enforce the superposition of two weighted sparsity constraints, one on the shared interactions and the other on the task-specific structural patterns. This enables JEEK to elegantly consider various forms of existing knowledge based on the domain at hand and avoid the need to design knowledge-specific optimization. JEEK is solved through a fast and entry-wise parallelizable solution that largely improves the computational efficiency of the state-of-the-art $O(p^5K^4)$ to $O(p^2K^4)$. We conduct a rigorous statistical analysis showing that JEEK achieves the same convergence rate $O(\log(Kp)/n_{tot})$ as the state-of-the-art estimators that are much harder to compute. Empirically, on multiple synthetic datasets and two real-world data, JEEK outperforms the speed of the state-of-arts significantly while achieving the same level of prediction accuracy.

About Adding Additional Knowledge

One significant caveat of state-of-the-art joint sGGM estimators is the fact that little attention has been paid to incorporating existing knowledge of the nodes or knowledge of the relationships among nodes in the models. In addition to the samples themselves, additional information is widely available in real-world applications. In fact, incorporating the knowledge is of great scientific interest. A prime example is when estimating the functional brain connectivity networks among brain regions based on fMRI samples, the spatial position of the regions are readily available. Neuroscientists have gathered considerable knowledge regarding the spatial and anatomical evidence underlying brain connectivity (e.g., short edges and certain anatomical regions are more likely to be connected \cite{watts1998collective}). Another important example is the problem of identifying gene-gene interactions from patients’ gene expression profiles across multiple cancer types. Learning the statistical dependencies among genes from such heterogeneous datasets can help to understand how such dependencies vary from normal to abnormal and help to discover contributing markers that influence or cause the diseases. Besides the patient samples, state-of-the-art bio-databases like HPRD \cite{prasad2009human} have collected a significant amount of information about direct physical interactions among corresponding proteins, regulatory gene pairs or signaling relationships collected from high-qualify bio-experiments.

Although being strong evidence of structural patterns we aim to discover, this type of information has rarely been considered in the joint sGGM formulation of such samples. This paper aims to fill this gap by adding additional knowledge most effectively into scalable and fast joint sGGM estimations.

The proposed JEEK estimator provides the flexibility of using ($K+1$) different weight matrices representing the extra knowledge. We try to showcase a few possible designs of the weight matrices, including (but not limited to):

  • Spatial or anatomy knowledge about brain regions;
  • Knowledge of known co-hub nodes or perturbed nodes;
  • Known group information about nodes, such as genes belonging to the same biological pathway or cellular location;
  • Using existing known edges as the knowledge, like the known protein interaction databases for discovering gene networks (a semi-supervised setting for such estimations).

We sincerely believe the scalability and flexibility provided by JEEK can make structure learning of joint sGGM feasible in many real-world tasks.

an example W for how to add known group sparity

jeekGroup

an example W for how to add known group interactions

jeekGroup

an example W for how to add known hub node

jeekGroup

an example W for how to add known perturbed-hub node

jeekGroup

Citations

@conference{wang2018jeek,
  Author = {Wang, Beilun and Sekhon, Arshdeep and Qi, Yanjun},
  Booktitle = {Proceedings of The 35th International Conference on Machine Learning (ICML)},
  Title = {A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models},
  Year = {2018}}
}

Support or Contact

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My Summary Talk about DeepChrome-AttentiveChrome-DeepMotif

Here are the slides of lecture talks I gave at UCLA CGWI and NLM-CBB seminar about our deep learning tools: DeepChrome, AttentiveChrome and DeepMotif.

Slides: PDF

Recorded Video of My Talk

Thanks for reading!



Feature Squeezing- Detecting Adversarial Examples in Deep Neural Networks

Paper Arxiv

GitHub: FeatureSqueezing

TalkSlide

Abstract

Although deep neural networks (DNNs) have achieved great success in many computer vision tasks, recent studies have shown they are vulnerable to adversarial examples. Such examples, typically generated by adding small but purposeful distortions, can frequently fool DNN models. Previous studies to defend against adversarial examples mostly focused on refining the DNN models. They have either shown limited success or suffer from the expensive computation. We propose a new strategy, \emph{feature squeezing}, that can be used to harden DNN models by detecting adversarial examples. Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample. By comparing a DNN model’s prediction on the original input with that on the squeezed input, feature squeezing detects adversarial examples with high accuracy and few false positives. This paper explores two instances of feature squeezing: reducing the color bit depth of each pixel and smoothing using a spatial filter. These strategies are straightforward, inexpensive, and complementary to defensive methods that operate on the underlying model, such as adversarial training.

evadePDF

Citations

@inproceedings{Xu0Q18,
  author    = {Weilin Xu and
               David Evans and
               Yanjun Qi},
  title     = {Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks},
  booktitle = {25th Annual Network and Distributed System Security Symposium, {NDSS}
               2018, San Diego, California, USA, February 18-21, 2018},
  year      = {2018},
  crossref  = {DBLP:conf/ndss/2018},
  url       = {http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/02/ndss2018\_03A-4\_Xu\_paper.pdf},
  timestamp = {Thu, 09 Aug 2018 10:57:16 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ndss/Xu0Q18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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Blackbox Generation of Adversarial Text Sequences

Title: Black-box Generation of Adversarial Text Sequences to Fool Deep Learning Classifiers

evadePDF

GitHub: https://github.com/QData/deepWordBug

TalkSlide: URL

Paper Arxiv

Published @ 2018 IEEE Security and Privacy Workshops (SPW), co-located with the 39th IEEE Symposium on Security and Privacy.

  • Extended version @ PDF

Abstract

Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to a black-box attack, which is a more realistic scenario. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We develop novel scoring strategies to find the most important words to modify such that the deep classifier makes a wrong prediction. Simple character-level transformations are applied to the highest-ranked words in order to minimize the edit distance of the perturbation. We evaluated DeepWordBug on two real-world text datasets: Enron spam emails and IMDB movie reviews. Our experimental results indicate that DeepWordBug can reduce the classification accuracy from 99% to around 40% on Enron data and from 87% to about 26% on IMDB. Also, our experimental results strongly demonstrate that the generated adversarial sequences from a deep-learning model can similarly evade other deep models.

We build an interactive extension to visualize DeepWordbug:

  • Interactive Live Demo @ ULR

evadePDF

Citations

@INPROCEEDINGS{JiDeepWordBug18, 
author={J. Gao and J. Lanchantin and M. L. Soffa and Y. Qi}, 
booktitle={2018 IEEE Security and Privacy Workshops (SPW)}, 
title={Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers}, 
year={2018}, 
pages={50-56}, 
keywords={learning (artificial intelligence);pattern classification;program debugging;text analysis;deep learning classifiers;character-level transformations;IMDB movie reviews;Enron spam emails;real-world text datasets;scoring strategies;text input;text perturbations;DeepWordBug;black-box attack;adversarial text sequences;black-box generation;Perturbation methods;Machine learning;Task analysis;Recurrent neural networks;Prediction algorithms;Sentiment analysis;adversarial samples;black box attack;text classification;misclassification;word embedding;deep learning}, 
doi={10.1109/SPW.2018.00016}, 
month={May},}

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EvadeML-Zoo Benchmarking and Visualization AE Tool is released

We are releasing EvadeML-Zoo: A Benchmarking and Visualization Tool for Adversarial Examples (with 8 pretrained deep models+ 9 state-of-art attacks).

Tool Github URL

evadePDF

About

We have designed and implemented EvadeML-Zoo, a benchmarking and visualization tool for research on adversarial machine learning. The goal of EvadeML-Zoo is to ease the experimental setup and help researchers evaluate and verify their results.

EvadeML-Zoo has a modular architecture and is designed to make it easy to add new datasets, pre-trained target models, attack or defense algorithms. The code is open source under the MIT license.

We have integrated three popular datasets: MNIST, CIFAR-10 and ImageNet- ILSVRC with a simple and unified interface. We offer several representative pre-trained models with state-of-the-art accuracy for each dataset including two pre-trained models for ImageNet-ILSVRC: the heavy Inception-v3 and and the lightweight MobileNet. We use Keras to access the pre-trained models because it provides a simplified interface and it is compatible with TensorFlow, which is a flexible tool for implementing attack and defense techniques.

We have integrated several existing attack algorithms as baseline for the upcoming new methods, including FGSM, BIM, JSMA, Deepfool, Universal Adversarial Perturbations, and Carlini and Wagner’s algorithms.

We have integrated our “feature squeezing” based detection framework in this toolbox. Formulating detecting adversarial examples as a binary classification task, we first construct a balanced dataset with equal number of legitimate and adversarial examples, and then split it into training and test subsets. A detection method has full access to the training set but no access to the labels of the test set. We measure the TPR and FPR on the test set as the benchmark detection results. Our Feature Squeezing functions as the detection baseline. Users can easily add more detection methods using our framework.

Besides, the tool comes with an interactive web-based visualization module adapted from our previous ADVERSARIAL-PLAYGROUND package. This module enables better understanding of the impact of attack algorithms on the resulting adversarial sample; users may specify attack algorithm parameters for a variety of attack types and generate new samples on-demand. The interface displays the resulting adversarial example as compared to the original, classification likelihoods, and the influence of a target model throughout layers of the network.

Citations

@inproceedings{Xu0Q18,
  author    = {Weilin Xu and
               David Evans and
               Yanjun Qi},
  title     = {Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks},
  booktitle = {25th Annual Network and Distributed System Security Symposium, {NDSS}
               2018, San Diego, California, USA, February 18-21, 2018},
  year      = {2018},
  crossref  = {DBLP:conf/ndss/2018},
  url       = {http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/02/ndss2018\_03A-4\_Xu\_paper.pdf},
  timestamp = {Thu, 09 Aug 2018 10:57:16 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ndss/Xu0Q18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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Best Paper Award for Deep Motif Dashboard

Jack’s DeepMotif paper (Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks ) have received the “best paper award“ at NIPS17 workshop for Transparent and interpretable Machine Learning in Safety Critical Environments. Big congratulations!!!



Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification

Prototype Matching Networks : A novel deep learning architecture for Large-Scale Multi-label Genomic Sequence Classification

Paper: @Arxiv

Abstract

One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechanisms for TF binding: (1) sequence-specific binding patterns on genomes known as “motifs” and (2) interactions among TFs known as co-binding effects. In this paper, we propose a novel deep architecture, the Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN model automatically extracts prototypes (“motif”-like features) for each TF through a novel prototype-matching loss. Borrowing ideas from few-shot matching models, we use the notion of support set of prototypes and an LSTM to learn how TFs interact and bind to genomic sequences. On a reference TFBS dataset with 2.1 million genomic sequences, PMN significantly outperforms baselines and validates our design choices empirically. To our knowledge, this is the first deep learning architecture that introduces prototype learning and considers TF-TF interactions for large-scale TFBS prediction. Not only is the proposed architecture accurate, but it also models the underlying biology.

Citations

@article{lanchantin2017prototype,
  title={Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification},
  author={Lanchantin, Jack and Sekhon, Arshdeep and Singh, Ritambhara and Qi, Yanjun},
  journal={arXiv preprint arXiv:1710.11238},
  year={2017}
}

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DIFFEE to identify Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Tool DIFFEE: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Paper: @Arxiv | at 2018 AISTAT

Presentation: Slides @ AISTAT18

Poster @ NIPS 2017 workshop for Advances in Modeling and Learning Interactions from Complex Data.

R package: GitHub

R package: CRAN

install.packages("diffee")
library(diffee)
demo(diffee)

Abstract

We focus on the problem of estimating the change in the dependency structures of two p-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.

DIFFEE

Citations

@InProceedings{pmlr-v84-wang18f,
  title =    {Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure},
  author =   {Beilun Wang and arshdeep Sekhon and Yanjun Qi},
  booktitle =    {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
  pages =    {1691--1700},
  year =   {2018},
  editor =   {Amos Storkey and Fernando Perez-Cruz},
  volume =   {84},
  series =   {Proceedings of Machine Learning Research},
  address =    {Playa Blanca, Lanzarote, Canary Islands},
  month =    {09--11 Apr},
  publisher =    {PMLR},
  pdf =    {http://proceedings.mlr.press/v84/wang18f/wang18f.pdf},
  url =    {http://proceedings.mlr.press/v84/wang18f.html},
  abstract =   {We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth optimization. We propose a novel method, DIFFEE for estimating DIFFerential networks via an Elementary Estimator under a high-dimensional situation. DIFFEE is solved through a faster and closed form solution that enables it to work in large-scale settings. We conduct a rigorous statistical analysis showing that surprisingly DIFFEE achieves the same asymptotic convergence rates as the state-of-the-art estimators that are much more difficult to compute. Our experimental results on multiple synthetic datasets and one real-world data about brain connectivity show strong performance improvements over baselines, as well as significant computational benefits.}
}

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W-SIMULE

Tool W-SIMULE: A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs with Additional Prior knowledge

We are updating the R package: simule with one more function: W-SIMULE

install.packages("simule")
library(simule)
demo(wsimule)

Package Manual

GitHub

Paper: @Arxiv @ NIPS 2017 workshop for Advances in Modeling and Learning Interactions from Complex Data.

Presentation: @Slides

Poster: @PDF

Abstract

Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphical models to learn brain connectivity via statistical dependencies across brain regions from neuroimaging. However, previous studies often fail to properly incorporate priors tailored to neuroscience, such as preferring shorter connections. To remedy this problem, the paper here introduces a novel, weighted-ℓ1, multi-task graphical model (W-SIMULE). This model elegantly incorporates a flexible prior, along with a parallelizable formulation. Additionally, W-SIMULE extends the often-used Gaussian assumption, leading to considerable performance increases. Here, applications to fMRI data show that W-SIMULE succeeds in determining functional connectivity in terms of (1) log-likelihood, (2) finding edges that differentiate groups, and (3) classifying different groups based on their connectivity, achieving 58.6\% accuracy on the ABIDE dataset. Having established W-SIMULE’s effectiveness, it links four key areas to autism, all of which are consistent with the literature. Due to its elegant domain adaptivity, W-SIMULE can be readily applied to various data types to effectively estimate connectivity.

W-SIMULE

W-SIMULE

Citations

@article{singh2017constrained,
  title={A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs},
  author={Singh, Chandan and Wang, Beilun and Qi, Yanjun},
  journal={arXiv preprint arXiv:1709.04090},
  year={2017}
}

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Adversarial-Playground Paper Appear @ VizSec17

Revised Version2 Paper Arxiv

Revised Title: Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning

Publish @ The IEEE Symposium on Visualization for Cyber Security (VizSec) 2017 -ULR

Presentation

Recorded Video of Andrew's Presentation

GitHub: AdversarialDNN-Playground

Abstract

Recent studies have shown that attackers can force deep learning models to misclassify so-called “adversarial examples”: maliciously generated images formed by making imperceptible modifications to pixel values. With growing interest in deep learning for security applications, it is important for security experts and users of machine learning to recognize how learning systems may be attacked. Due to the complex nature of deep learning, it is challenging to understand how deep models can be fooled by adversarial examples. Thus, we present a web-based visualization tool, Adversarial-Playground, to demonstrate the efficacy of common adversarial methods against a convolutional neural network (CNN) system. Adversarial-Playground is educational, modular and interactive. (1) It enables non-experts to compare examples visually and to understand why an adversarial example can fool a CNN-based image classifier. (2) It can help security experts explore more vulnerability of deep learning as a software module. (3) Building an interactive visualization is challenging in this domain due to the large feature space of image classification (generating adversarial examples is slow in general and visualizing images are costly). Through multiple novel design choices, our tool can provide fast and accurate responses to user requests. Empirically, we find that our client-server division strategy reduced the response time by an average of 1.5 seconds per sample. Our other innovation, a faster variant of JSMA evasion algorithm, empirically performed twice as fast as JSMA and yet maintains a comparable evasion rate. Project source code and data from our experiments available at: GitHub

Citations

@inproceedings{norton2017adversarial,
  title={Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning},
  author={Norton, Andrew P and Qi, Yanjun},
  booktitle={Visualization for Cyber Security (VizSec), 2017 IEEE Symposium on},
  pages={1--4},
  year={2017},
  organization={IEEE}
}

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Feature Squeezing Mitigates and Detects Carlini-Wagner Adversarial Examples

Paper Arxiv

Abstract

Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

evadePDF

Citations

@article{xu2017feature,
  title={Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples},
  author={Xu, Weilin and Evans, David and Qi, Yanjun},
  journal={arXiv preprint arXiv:1705.10686},
  year={2017}
}

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AttentiveChrome-Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin

Tool AttentiveChrome: Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin

Paper: @Arxiv + Published at [NIPS2017]

(https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf)

GitHub

talk slides PDF

poster PDF

Abstract:

The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together? Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; we call AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long short-term memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map. Code and data are shared at www.deepchrome.org

attentiveChrome

attentiveChrome

Citations

@inproceedings{singh2017attend,
  title={Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin},
  author={Singh, Ritambhara and Lanchantin, Jack and Sekhon, Arshdeep  and Qi, Yanjun},
  booktitle={Advances in Neural Information Processing Systems},
  pages={6769--6779},
  year={2017}
}

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Memory Matching Networks for Genomic Sequence Classification

Tool Memory Matching Networks for Genomic Sequence Classification

Paper: @Arxiv

GitHub

Poster

Abstract

When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as “motifs”. However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning over inputs and supporting sets. In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites. Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or nonbinding site.

memo

Citations

@article{lanchantin2017memory,
  title={Memory Matching Networks for Genomic Sequence Classification},
  author={Lanchantin, Jack and Singh, Ritambhara and Qi, Yanjun},
  journal={arXiv preprint arXiv:1702.06760},
  year={2017}
}

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Deep Motif Dashboard- Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

Tool Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

Paper: @Arxiv | @PSB17

GitHub

Talk Slides

Abstract:

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

demo1 demo2 demo3 demo4

Citations

@inproceedings{lanchantin2017deep,
  title={Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks},
  author={Lanchantin, Jack and Singh, Ritambhara and Wang, Beilun and Qi, Yanjun},
  booktitle={PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017},
  pages={254--265},
  year={2017},
  organization={World Scientific}
}

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DeepChrome- deep-learning for predicting gene expression from histone modifications

Tool DeepChrome: deep-learning for predicting gene expression from histone modifications

Paper: @Bioinformatics

GitHub

Talk Slides

Abstract:

Motivation: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation. This knowledge can help in developing ‘epigenetic drugs’ for diseases like cancer. Previous studies for quantifying the relationship between histone modifications and gene expression levels either failed to capture combinatorial effects or relied on multiple methods that separate predictions and combinatorial analysis. This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input. Our system, called DeepChrome, allows automatic extraction of complex interactions among important features. To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model. This provides an intuitive description of underlying epigenetic mechanisms that regulate genes. Results: We show that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database. The output of our visualization technique not only validates the previous observations but also allows novel insights about combinatorial interactions among histone modification marks, some of which have recently been observed by experimental studies.

dp1 dp2

Citations

@article{singh2016deepchrome,
  title={DeepChrome: deep-learning for predicting gene expression from histone modifications},
  author={Singh, Ritambhara and Lanchantin, Jack and Robins, Gabriel and Qi, Yanjun},
  journal={Bioinformatics},
  volume={32},
  number={17},
  pages={i639--i648},
  year={2016},
  publisher={Oxford University Press}
}

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Adversarial-Playground- A Visualization Suite for Adversarial Sample Generation

Paper Arxiv

GitHub: AdversarialDNN-Playground

Poster

Abstract

With growing interest in adversarial machine learning, it is important for machine learning practitioners and users to understand how their models may be attacked. We propose a web-based visualization tool, \textit{Adversarial-Playground}, to demonstrate the efficacy of common adversarial methods against a deep neural network (DNN) model, built on top of the TensorFlow library. Adversarial-Playground provides users an efficient and effective experience in exploring techniques generating adversarial examples, which are inputs crafted by an adversary to fool a machine learning system. To enable Adversarial-Playground to generate quick and accurate responses for users, we use two primary tactics: (1) We propose a faster variant of the state-of-the-art Jacobian saliency map approach that maintains a comparable evasion rate. (2) Our visualization does not transmit the generated adversarial images to the client, but rather only the matrix describing the sample and the vector representing classification likelihoods.

Playground

pg pg

Citations

@inproceedings{norton2017adversarial,
  title={Adversarial-Playground: A visualization suite showing how adversarial examples fool deep learning},
  author={Norton, Andrew P and Qi, Yanjun},
  booktitle={Visualization for Cyber Security (VizSec), 2017 IEEE Symposium on},
  pages={1--4},
  year={2017},
  organization={IEEE}
}

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DeepCloak- Masking Deep Neural Network Models for Robustness against Adversarial Samples

Paper ICLR17 Workshop

GitHub: DeepCloak

Poster

Abstract

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepCloak. By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepCloak is easy to implement and computationally efficient. Experimental results show that DeepCloak can increase the performance of state-of-the-art DNN models against adversarial samples.

deepCloak

Citations

@article{gao2017deepmask,
  title={DeepCloak: Masking DNN Models for robustness against adversarial samples},
  author={Gao, Ji and Wang, Beilun and Qi, Yanjun},
  journal={arXiv preprint arXiv:1702.06763},
  year={2017}
}

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A Tool for Automatically Evading Classifiers for PDF Malware detection

Paper: Automatically Evading Classifiers,

A Case Study on PDF Malware Classifiers NDSS16

More information is provided by EvadeML.org

By using evolutionary techniques to simulate an adversary’s efforts to evade that classifier

GitHub: EvadePDFClassifiers

Presentation

Abstract

Machine learning is widely used to develop classifiers for security tasks. However, the robustness of these methods against motivated adversaries is uncertain. In this work, we propose a generic method to evaluate the robustness of classifiers under attack. The key idea is to stochastically manipulate a malicious sample to find a variant that preserves the malicious behavior but is classified as benign by the classifier. We present a general approach to search for evasive variants and report on results from experiments using our techniques against two PDF malware classifiers, PDFrate and Hidost. Our method is able to automatically find evasive variants for both classifiers for all of the 500 malicious seeds in our study. Our results suggest a general method for evaluating classifiers used in security applications, and raise serious doubts about the effectiveness of classifiers based on superficial features in the presence of adversaries.

evadePDF

Citations

@inproceedings{xu2016automatically,
  title={Automatically evading classifiers},
  author={Xu, Weilin and Qi, Yanjun and Evans, David},
  booktitle={Proceedings of the 2016 Network and Distributed Systems Symposium},
  year={2016}
}

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A Theoretical Framework for Robustness of (Deep) Classifiers Against Adversarial Samples

Paper ICLR17 workshop

Poster

Abstract

Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifications that lead to incorrect outputs while imperceptible to human eyes. The goal of this paper is not to introduce a single method, but to make theoretical steps towards fully understanding adversarial examples. By using concepts from topology, our theoretical analysis brings forth the key reasons why an adversarial example can fool a classifier (f1) and adds its oracle (f2, like human eyes) in such analysis. By investigating the topological relationship between two (pseudo)metric spaces corresponding to predictor f1 and oracle f2, we develop necessary and sufficient conditions that can determine if f1 is always robust (strong-robust) against adversarial examples according to f2. Interestingly our theorems indicate that just one unnecessary feature can make f1 not strong-robust, and the right feature representation learning is the key to getting a classifier that is both accurate and strong-robust.

Recent studies are mostly empirical and provide little understanding of why an adversary can fool machine learning models with adversarial examples. Several important questions have not been answered yet:

  • What makes a classifier always robust to adversarial examples?
  • Which parts of a classifier influence its robustness against adversarial examples more, compared with the rest?
  • What is the relationship between a classifier’s generalization accuracy and its robustness against adversarial examples?
  • Why (many) DNN classifiers are not robust against adversarial examples ? How to improve?

This paper uses the following framework

  • to understand adversarial examples (by considering the role of oracle): oracle

  • The following figure provides a simple case illustration explaining unnecessary features make a classifier vulnerable to adversarial examples: unnecessaryfeatures

  • The following figure tries to explain why DNN models are vulnerable to adversarial examples: unnecessaryfeatures

Citations

@article{wang2016theoretical,
  title={A theoretical framework for robustness of (deep) classifiers under adversarial noise},
  author={Wang, Beilun and Gao, Ji and Qi, Yanjun},
  journal={arXiv preprint},
  year={2016}
}

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GaKCo-SVM- a Fast GApped k-mer string Kernel using COunting

Tool GaKCo-SVM: a Fast GApped k-mer string Kernel using COunting

Paper: @Arxiv | @ECML17

GitHub

Talk PDF

Poster

Abstract:

String Kernel (SK) techniques, especially those using gapped k-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (Σ) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional to O(ΣM). We propose a \textbf{fast} algorithm for calculating \underline{Ga}pped k-mer \underline{K}ernel using \underline{Co}unting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to larger Σ and M, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of the ΣM term that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets), respectively.

gakco

Citations

@inproceedings{singh_gakco:_2017,
	location = {Cham},
	title = {GaKCo: A Fast Gapped k-mer String Kernel Using Counting},
	isbn = {978-3-319-71249-9},
	pages = {356--373},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	publisher = {Springer International Publishing},
	author = {Singh, Ritambhara and Sekhon, Arshdeep and Kowsari, Kamran and Lanchantin, Jack and Wang, Beilun and Qi, Yanjun},
	editor = {Ceci, Michelangelo and Hollmén, Jaakko and Todorovski, Ljupčo and Vens, Celine and Džeroski, Sašo},
	date = {2017}
}

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TSK- Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction

Tool TSK: Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction

Paper

GitHub

Abstract

Through sequence-based classification, this paper tries to accurately predict the DNA binding sites of transcription factors (TFs) in an unannotated cellular context. Related methods in the literature fail to perform such predictions accurately, since they do not consider sample distribution shift of sequence segments from an annotated (source) context to an unannotated (target) context. We, therefore, propose a method called “Transfer String Kernel” (TSK) that achieves improved prediction of transcription factor binding site (TFBS) using knowledge transfer via cross-context sample adaptation. TSK maps sequence segments to a high-dimensional feature space using a discriminative mismatch string kernel framework. In this high-dimensional space, labeled examples of the source context are re-weighted so that the revised sample distribution matches the target context more closely. We have experimentally verified TSK for TFBS identifications on fourteen different TFs under a cross-organism setting. We find that TSK consistently outperforms the state-of the-art TFBS tools, especially when working with TFs whose binding sequences are not conserved across contexts. We also demonstrate the generalizability of TSK by showing its cutting-edge performance on a different set of cross-context tasks for the MHC peptide binding predictions.

TSK

Citations

@article{singh2016transfer,
  title={Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction},
  author={Singh, Ritambhara and Lanchantin, Jack and Robins, Gabriel and Qi, Yanjun},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2016},
  publisher={IEEE}
}

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FASJEM R package is released!

R package: fasjem

install.packages("fasjem")
library(fasjem)
demo(fasjem)

Package Manual

Paper: @AISTAT17 | @Arxiv

GitHub

Talk URL

Poster

Abstract

Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large K) under a high-dimensional (large p) situation is an important task. Most previous studies for the joint estimation of multiple sGGMs rely on penalized log-likelihood estimators that involve expensive and difficult non-smooth optimizations. We propose a novel approach, FASJEM for fast and scalable joint structure-estimation of multiple sGGMs at a large scale. As the first study of joint sGGM using the M-estimator framework, our work has three major contributions: (1) We solve FASJEM through an entry-wise manner which is parallelizable. (2) We choose a proximal algorithm to optimize FASJEM. This improves the computational efficiency from O(Kp3 ) to O(Kp2 ) and reduces the memory requirement from O(Kp2 ) to O(K). (3) We theoretically prove that FASJEM achieves a consistent estimation with a convergence rate of O(log(Kp)/ntot). On several synthetic and four real-world datasets, FASJEM shows significant improvements over baselines on accuracy, computational complexity and memory costs.

JEM

JEM2

JEMmore

Citations

@inproceedings{wang2017fast,
  title={A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models},
  author={Wang, Beilun and Gao, Ji and Qi, Yanjun},
  booktitle={Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR:, 2017.},
  volume={54},
  pages={1168--1177},
  year={2017}
}

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SIMULE R package is released!

Tool SIMULE: A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models

R package: simule

install.packages("simule")
library(simule)
demo(simule)

Package Manual

GitHub

Paper: @Arxiv | @Mach Learning

Talk

Abstract

Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can’t identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming that can be solved efficiently in parallel. In addition to Gaussian data, SIMULE can also handle multivariate Nonparanormal data that greatly relaxes the normality assumption that many real-world applications do not follow. We provide a novel theoretical proof showing that SIMULE achieves a consistent result at the rate O(log(Kp)/n_{tot}). On multiple synthetic datasets and two biomedical datasets, SIMULE shows significant improvement over state-of-the-art multi-sGGM and single-UGM baselines.

SIMULE

Citations

@Article{Wang2017,
author="Wang, Beilun and Singh, Ritambhara and Qi, Yanjun",
title="A constrained L1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models",
journal="Machine Learning",
year="2017",
month="Oct",
day="01",
volume="106",
number="9",
pages="1381--1417",
abstract="Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging applications. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse undirected graphical models(UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian graphical models (sGGMs) and can't identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained  L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the  L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming that can be solved efficiently in parallel. In addition to Gaussian data, SIMULE can also handle multivariate Nonparanormal data that greatly relaxes the normality assumption that many real-world applications do not follow. We provide a novel theoretical proof showing that SIMULE achieves a consistent result at the rate  
log (Kp)/(n_tot). On multiple synthetic datasets and two biomedical datasets, SIMULE shows significant improvement over state-of-the-art multi-sGGM and single-UGM baselines 
(SIMULE implementation and the used datasets @  https://github.com/QData/SIMULE  ).",
issn="1573-0565",
doi="10.1007/s10994-017-5635-7",
url="https://doi.org/10.1007/s10994-017-5635-7"
}

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MUST-CNN- A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

Tool MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

Paper

GitHub

Talk Slides

Abstract

Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.

must1 must2 must3 must4

Citations

@inproceedings{lin2016must,
  title={MUST-CNN: a multilayer shift-and-stitch deep convolutional architecture for sequence-based protein structure prediction},
  author={Lin, Zeming and Lanchantin, Jack and Qi, Yanjun},
  booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
  pages={27--34},
  year={2016},
  organization={AAAI Press}
}

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Learning the Dependency Structure of Latent Factors

Paper: @NeurIPS12

  • Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park

GitHub

Poster PDF

Abstract:

In this paper, we study latent factor models with the dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. The main benefit (novelty) of the model is that we can simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is devised to make the model feasible for large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by our model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.

slf

slf

slf

Citations

@inproceedings{he2012learning,
  title={Learning the dependency structure of latent factors},
  author={He, Yunlong and Qi, Yanjun and Kavukcuoglu, Koray and Park, Haesun},
  booktitle={Advances in neural information processing systems},
  pages={2366--2374},
  year={2012}
}

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Unsupervised Feature Learning by Deep Sparse Coding

Paper: @Arxiv

  • Y He, K Kavukcuoglu, Y Wang, A Szlam, Y Qi

Talk PDF

Abstract:

In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial pooling step and a low-dimensional embedding process, which takes advantage of the spatial smoothness information in the image. As a result, the new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. Combining the feature representations from multiple layers, DeepSC achieves the state-of-the-art performance on multiple object recognition tasks.

gakco

Citations

@misc{he2013unsupervised,
    title={Unsupervised Feature Learning by Deep Sparse Coding},
    author={Yunlong He and Koray Kavukcuoglu and Yun Wang and Arthur Szlam and Yanjun Qi},
    year={2013},
    eprint={1312.5783},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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Deep Learning for Character-based Information Extraction on Chinese and Protein Sequence

Title: Deep Learning for Character-based Information Extraction on Chinese and Protein Sequence

  • authors: Yanjun Qi, Sujatha Das, Ronan Collobert, Jason Weston

Paper ECIR

Supplementary Here

Talk: Slide

Abstract

In this paper we introduce a deep neural network architecture to perform information extraction on character-based sequences, e.g. named-entity recognition on Chinese text or secondary-structure detection on protein sequences. With a task-independent architecture, the deep network relies only on simple character-based features, which obviates the need for task-specific feature engineering. The proposed discriminative framework includes three important strategies, (1) a deep learning module mapping characters to vector representations is included to capture the semantic relationship between characters; (2) abundant online sequences (unlabeled) are utilized to improve the vector representation through semi-supervised learning; and (3) the constraints of spatial dependency among output labels are modeled explicitly in the deep architecture. The experiments on four benchmark datasets have demonstrated that, the proposed architecture consistently leads to the state-of-the-art performance.

Citations

@inproceedings{qi2014deep,
  title={Deep learning for character-based information extraction},
  author={Qi, Yanjun and Das, Sujatha G and Collobert, Ronan and Weston, Jason},
  booktitle={European Conference on Information Retrieval},
  pages={668--674},
  year={2014},
  organization={Springer}
}

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My Talk in 2013 about our DeepLearning Works on Protein and BioNLP datasets

Here are the slides of one lecture talk I gave at UVA CPHG Seminar Series in 2014 about our deep learning tools back then.

Slides: @URL

timeline

Thanks for reading!



A unified multitask architecture for predicting local structural properties on proteins

Tool Multitask-ProteinTagging: A unified multitask architecture for predicting local protein properties

Paper

GitHub

Abstract

A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance.

multi

multi

Citations

@article{qi12plosone,
    author = {Qi, , Yanjun AND Oja, , Merja AND Weston, , Jason AND Noble, , William Stafford},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
    title = {A Unified Multitask Architecture for Predicting Local Protein Properties},
    year = {2012},
    month = {03},
    volume = {7},
    url = {http://dx.doi.org/10.1371%2Fjournal.pone.0032235},
    pages = {e32235},
    number = {3},
    doi = {10.1371/journal.pone.0032235}
}        

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A few other MRF tools we built

Paper1: Learning the Dependency Structure of Latent Factors

  • Y. He, Y. Qi, K. Kavukcuoglu, H. Park (2012) NeurIPS
  • PDF
  • Talk: Slide

  • Abstract In this paper, we study latent factor models with the dependency structure in the latent space. We propose a general learning framework which induces sparsity on the undirected graphical model imposed on the vector of latent factors. A novel latent factor model SLFA is then proposed as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. The main benefit (novelty) of the model is that we can simultaneously learn the lower-dimensional representation for data and model the pairwise relationships between latent factors explicitly. An on-line learning algorithm is devised to make the model feasible for large-scale learning problems. Experimental results on two synthetic data and two real-world data sets demonstrate that pairwise relationships and latent factors learned by our model provide a more structured way of exploring high-dimensional data, and the learned representations achieve the state-of-the-art classification performance.

Citations

@INPROCEEDINGS{yhe12NIPS,
  title={Learning the Dependency Structure of Latent Factors},
  author={Y. He and Y. Qi and K. Kavukcuoglu and H. Park},
  booktitle={Proceedings of Advances in Neural Information Processing Systems (NIPS)},
  year={2012},
  note="{\\Acceptance rate = 25\% (370/1467)}"
}

Paper2: Sparse higher-order Markov random field

  • PDF

  • Abstract Systems and methods are provided for identifying combinatorial feature interactions, including capturing statistical dependencies between categorical variables, with the statistical dependencies being stored in a computer readable storage medium. A model is selected based on the statistical dependencies using a neighborhood estimation strategy, with the neighborhood estimation strategy including generating sets of arbitrarily high-order feature interactions using at least one rule forest and optimizing one or more likelihood functions. A damped mean-field approach is applied to the model to obtain parameters of a Markov random field (MRF); a sparse high-order semi-restricted MRF is produced by adding a hidden layer to the MRF; indirect long-range dependencies between feature groups are modeled using the sparse high-order semi-restricted MRF; and a combinatorial dependency structure between variables is output.

Citations

@misc{min2015sparse,
  title={Sparse higher-order Markov random field},
  author={Min, Renqiang and Qi, Yanjun},
  year={2015},
  month=nov # "~10",
  publisher={Google Patents},
  note={US Patent 9,183,503}
}

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Deep Metric Learning to Learn and to Use

Paper0: Learning to rank with (a lot of) word features

  • PDF

  • Abstract In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.

Paper1: Polynomial semantic indexing

  • PDF
  • Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Corinna Cortes, Mehryar Mohri
  • 2009 Conference on Advances in Neural Information Processing Systems
  • Abstract We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Dealing with polynomial models on word features is computationally challenging. We propose a low rank (but diagonal preserving) representation of our polynomial models to induce feasible memory and computation requirements. We provide an empirical study on retrieval tasks based on Wikipedia documents, where we obtain state-of-the-art performance while providing realistically scalable methods.

Paper2: Retrieving Medical Records with “sennamed”: NEC Labs America at TREC 2012 Medical Records Track

  • PDF

  • Abstract In this notebook, we describe the automatic retrieval runs from NEC Laboratories America (NECLA) for the Text REtrieval Conference (TREC) 2012 Medical Records track. Our approach is based on a combination of UMLS medical concept detection and a set of simple retrieval models. Our best run, sennamed2, has achieved the best inferred average precision (infAP) score on 5 of the 47 test topics, and obtained a higher score than the median of all submission runs on 27 other topics. Overall, sennamed2 ranks at the second place amongst all the 82 automatic runs submitted for this track, and obtains the third place amongst both automatic and manual submissions.

Paper3: Kernelized information-theoretic metric learning for cancer diagnosis using high-dimensional molecular profiling data

  • PDF

  • Abstract With the advancement of genome-wide monitoring technologies, molecular expression data have become widely used for diagnosing cancer through tumor or blood samples. When mining molecular signature data, the process of comparing samples through an adaptive distance function is fundamental but difficult, as such datasets are normally heterogeneous and high dimensional. In this article, we present kernelized information-theoretic metric learning (KITML) algorithms that optimize a distance function to tackle the cancer diagnosis problem and scale to high dimensionality. By learning a nonlinear transformation in the input space implicitly through kernelization, KITML permits efficient optimization, low storage, and improved learning of distance metric. We propose two novel applications of KITML for diagnosing cancer using high-dimensional molecular profiling data: (1) for sample-level cancer diagnosis, the learned metric is used to improve the performance of k-nearest neighbor classification; and (2) for estimating the severity level or stage of a group of samples, we propose a novel set-based ranking approach to extend KITML. For the sample-level cancer classification task, we have evaluated on 14 cancer gene microarray datasets and compared with eight other state-of-the-art approaches. The results show that our approach achieves the best overall performance for the task of molecular-expression-driven cancer sample diagnosis. For the group-level cancer stage estimation, we test the proposed set-KITML approach using three multi-stage cancer microarray datasets, and correctly estimated the stages of sample groups for all three studies.

Paper4: Learning preferences with millions of parameters by enforcing sparsity

  • PDF
  • Talk

  • Abstract We study the retrieval task that ranks a set of objects for a given query in the pair wise preference learning framework. Recently researchers found out that raw features (e.g. words for text retrieval) and their pair wise features which describe relationships between two raw features (e.g. word synonymy or polysemy) could greatly improve the retrieval precision. However, most existing methods can not scale up to problems with many raw features (e.g. English vocabulary), due to the prohibitive computational cost on learning and the memory requirement to store a quadratic number of parameters. In this paper, we propose to learn a sparse representation of the pair wise features under the preference learning framework using the L1 regularization. Based on stochastic gradient descent, an online algorithm is devised to enforce the sparsity using a mini-batch shrinkage strategy. On multiple benchmark datasets, we show that our method achieves better performance with fast convergence, and takes much less memory on models with millions of parameters.

Citations

@techreport{qi2012retrieving,
  title={Retrieving medical records with sennamed: Nec labs america at trec 2012 medical records track},
  author={Qi, Yanjun and Laquerre, Pierre-Fran{\c{c}}ois},
  year={2012},
  institution={NEC Laboratories America Inc Princeton NJ}
}

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Document classification with weighted supervised n-gram embedding

Summary:

  • Methods and systems for document classification include embedding n-grams from an input text in a latent space, embedding the input text in the latent space based on the embedded n-grams and weighting said n-grams according to spatial evidence of the respective n-grams in the input text, classifying the document along one or more axes, and adjusting weights used to weight the n-grams based on the output of the classifying step.

  • authors: Qi, Yanjun and Bai, Bing

Paper1: Sentiment classification with supervised sequence embedding

  • PDF
  • Talk: Slide

  • Abstract In this paper, we introduce a novel approach for modeling n-grams in a latent space learned from supervised signals. The proposed procedure uses only unigram features to model short phrases (n-grams) in the latent space. The phrases are then combined to form document-level latent representation for a given text, where position of an n-gram in the document is used to compute corresponding combining weight. The resulting two-stage supervised embedding is then coupled with a classifier to form an end-to-end system that we apply to the large-scale sentiment classification task. The proposed model does not require feature selection to retain effective features during pre-processing, and its parameter space grows linearly with size of n-gram. We present comparative evaluations of this method using two large-scale datasets for sentiment classification in online reviews (Amazon and TripAdvisor). The proposed method outperforms standard baselines that rely on bag-of-words representation populated with n-gram features.

Paper2: Sentiment Classification Based on Supervised Latent n-gram Analysis

  • PDF
  • Talk: Slide

  • Abstract In this paper, we propose an efficient embedding for modeling higher-order (n-gram) phrases that projects the n-grams to low-dimensional latent semantic space, where a classification function can be defined. We utilize a deep neural network to build a unified discriminative framework that allows for estimating the parameters of the latent space as well as the classification function with a bias for the target classification task at hand. We apply the framework to large-scale sentimental classification task. We present comparative evaluation of the proposed method on two (large) benchmark data sets for online product reviews. The proposed method achieves superior performance in comparison to the state of the art.

Citations

@misc{qi2014document,
  title={Document classification with weighted supervised n-gram embedding},
  author={Qi, Yanjun and Bai, Bing},
  year={2014},
  month=nov # "~18",
  publisher={Google Patents},
  note={US Patent 8,892,488}
}

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Systems and methods for semi-supervised relationship extraction

Title: Systems and methods for semi-supervised relationship extraction

  • authors: Qi, Yanjun and Bai, Bing and Ning, Xia and Kuksa, Pavel

Paper1: Semi-supervised abstraction-augmented string kernel for multi-level bio-relation extraction

  • PDF
  • Talk: Slide

  • Abstract Bio-relation extraction (bRE), an important goal in bio-text mining, involves subtasks identifying relationships between bio-entities in text at multiple levels, e.g., at the article, sentence or relation level. A key limitation of current bRE systems is that they are restricted by the availability of annotated corpora. In this work we introduce a semi-supervised approach that can tackle multi-level bRE via string comparisons with mismatches in the string kernel framework. Our string kernel implements an abstraction step, which groups similar words to generate more abstract entities, which can be learnt with unlabeled data. Specifically, two unsupervised models are proposed to capture contextual (local or global) semantic similarities between words from a large unannotated corpus. This Abstraction-augmented String Kernel (ASK) allows for better generalization of patterns learned from annotated data and provides a unified framework for solving bRE with multiple degrees of detail. ASK shows effective improvements over classic string kernels on four datasets and achieves state-of-the-art bRE performance without the need for complex linguistic features.

ask1 ask2

Paper2: Semi-Supervised Convolution Graph Kernels for Relation Extraction

  • PDF

  • Talk: Slide
  • URL More

  • Abstract Extracting semantic relations between entities is an important step towards automatic text understanding. In this paper, we propose a novel Semi-supervised Convolution Graph Kernel (SCGK) method for semantic Relation Extraction (RE) from natural language. By encoding English sentences as dependence graphs among words, SCGK computes kernels (similarities) between sentences using a convolution strategy, i.e., calculating similarities over all possible short single paths from two dependence graphs. Furthermore, SCGK adds three semi-supervised strategies in the kernel calculation to incorporate soft-matches between (1) words, (2) grammatical dependencies, and (3) entire sentences, respectively. From a large unannotated corpus, these semi-supervision steps learn to capture contextual semantic patterns of elements in natural sentences, which therefore alleviate the lack of annotated examples in most RE corpora. Through convolutions and multi-level semi-supervisions, SCGK provides a powerful model to encode both syntactic and semantic evidence existing in natural English sentences, which effectively recovers the target relational patterns of interest. We perform extensive experiments on five RE benchmark datasets which aim to identify interaction relations from biomedical literature. Our results demonstrate that SCGK achieves the state-of-the-art performance on the task of semantic relation extraction.

Paper3: Semi-Supervised Bio-Named Entity Recognition with Word-Codebook Learning

  • Pavel P. Kuksa, Yanjun Qi,
  • PDF

  • Abstract We describe a novel semi-supervised method called WordCodebook Learning (WCL), and apply it to the task of bionamed entity recognition (bioNER). Typical bioNER systems can be seen as tasks of assigning labels to words in bioliterature text. To improve supervised tagging, WCL learns a class of word-level feature embeddings to capture word semantic meanings or word label patterns from a large unlabeled corpus. Words are then clustered according to their embedding vectors through a vector quantization step, where each word is assigned into one of the codewords in a codebook. Finally codewords are treated as new word attributes and are added for entity labeling. Two types of wordcodebook learning are proposed: (1) General WCL, where an unsupervised method uses contextual semantic similarity of words to learn accurate word representations; (2) Task-oriented WCL, where for every word a semi-supervised method learns target-class label patterns from unlabeled data using supervised signals from trained bioNER model. Without the need for complex linguistic features, we demonstrate utility of WCL on the BioCreativeII gene name recognition competition data, where WCL yields state-of-the-art performance and shows great improvements over supervised baselines and semi-supervised counter peers.

Citations

@INPROCEEDINGS{ecml2010ask,
  author = {Pavel P. Kuksa and Yanjun Qi and Bing Bai and Ronan Collobert and
	Jason Weston and Vladimir Pavlovic and Xia Ning},
  title = {Semi-Supervised Abstraction-Augmented String Kernel for Multi-Level
	Bio-Relation Extraction},
  booktitle = {ECML},
  year = {2010},
  note = {Acceptance rate: 106/658 (16%)},
  bib2html_pubtype = {Refereed Conference},
}

gsk1 gsk2 gsk3 gsk4 gsk5

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Semi-Supervised Sequence Labeling with Self-Learned Feature

Title: Semi-Supervised Sequence Labeling with Self-Learned Feature

  • authors: Yanjun 
Qi,
Pavel
P 
Kuksa,
 Ronan 
Collobert, Kunihiko
 Sadamasa,
 Koray
 Kavukcuoglu,
 Jason 
Weston

Paper ICDM

Talk: Slide

Abstract

Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of labeled words. To tackle this issue, we propose a semi-supervised approach to improve the sequence labeling procedure in IE through a class of algorithms with self-learned features (SLF). A supervised classifier can be trained with annotated text sequences and used to classify each word in a large set of unannotated sentences. By averaging predicted labels over all cases in the unlabeled corpus, SLF training builds class label distribution patterns for each word (or word attribute) in the dictionary and re-trains the current model iteratively adding these distributions as extra word features. Basic SLF models how likely a word could be assigned to target class types. Several extensions are proposed, such as learning words’ class boundary distributions. SLF exhibits robust and scalable behaviour and is easy to tune. We applied this approach on four classical IE tasks: named entity recognition (German and English), part-of-speech tagging (English) and one gene name recognition corpus. Experimental results show effective improvements over the supervised baselines on all tasks. In addition, when compared with the closely related self-training idea, this approach shows favorable advantages.

Citations

@inproceedings{qi2009semi,
  title={Semi-supervised sequence labeling with self-learned features},
  author={Qi, Yanjun and Kuksa, Pavel and Collobert, Ronan and Sadamasa, Kunihiko and Kavukcuoglu, Koray and Weston, Jason},
  booktitle={2009 Ninth IEEE International Conference on Data Mining},
  pages={428--437},
  year={2009},
  organization={IEEE}
}

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Semi-supervised multi-task learning Using BioText based Labels to Augument PPI Prediction

Title: Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins

  • authors: Yanjun Qi, Oznur Tastan, Jaime G. Carbonell, Judith Klein-Seetharaman, Jason Weston

Paper Bioinformatics

Talk: Slide

Abstract

  • Motivation: Protein–protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction (partially labeled).

  • Results: We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information.

Citations

@article{qi2010semi,
  title={Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins},
  author={Qi, Yanjun and Tastan, Oznur and Carbonell, Jaime G and Klein-Seetharaman, Judith and Weston, Jason},
  journal={Bioinformatics},
  volume={26},
  number={18},
  pages={i645--i652},
  year={2010},
  publisher={Oxford University Press}
}

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