Zhe’s PhD Defense - Toward Out-Of-Distribution Generalization Of Deep Learning Models
Ph.D. Dissertation Defense by Zhe Wang, Tues., 04/02/24, at 12:00PM (ET) Committee:
Projects by Category (grid view)
AIself
Arsh’s PhD Defense - Relational Structure Discovery for Deep Learning
Arshdeep Sekhon’s PhD Defense June 29, 2022.
JackL’s PhD Defense - Modeling interactions with Deep Learning
Ph.D. Dissertation Defense by Jack Lanchantin Tuesday, July 20th, 2021 at 2:00 PM (ET), via Zoom. Committee: Vicente Ordóñez Román, Committee Chair, ...
ACM BCB - Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences
CVPR - General Multi-label Image Classification with Transformers
Title: General Multi-label Image Classification with Transformers
AAAI - Curriculum Labeling- Self-paced Pseudo-Labeling for Semi-Supervised Learning
Title: Curriculum Labeling- Self-paced Pseudo-Labeling for Semi-Supervised Learning”
EMNLP - Benchmarking Search Algorithms for Generating NLP Adversarial Examples
Title: Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples
EMNLP- On Quality of Generated Adversarial Examples and How to Set Attack Contraints
Title: Reevaluating Adversarial Examples in Natural Language
Beilun’s PhD Defense - Fast and Scalable Joint Estimators for Learning Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowledge
PhD Defense Presentation by Beilun Wang Friday, July 20, 2018 at 9:00 am in Rice 242 Committee Members: Mohammad Mahmoody (Chair), Yanjun Qi (Advisor), ...
AAAI - 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
ECIR - 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
Plos- A unified multitask architecture for predicting local structural properties on proteins
Tool Multitask-ProteinTagging: A unified multitask architecture for predicting local protein properties
NeurIPS - Deep Metric Learning to Learn and to Use
Paper0: Learning to rank with (a lot of) word features
AIbiomed
DrQi’s tutorial talk on “Make Deep Learning Interpretable for Sequential Data Analysis in Biomedicine” (Including our work on DeepChrome - AttentiveChrome - GCNChrome - DeepMotif - DeepVHPPI - MotifTransformer)
I gave a tutorial talk at UVA-VADC Seminar Series 2021 and at monthly NIH Data Science Showcase seminar.
ACM BCB - Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences
DrQi’s 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.
Bioinformatics - DeepDiff- Deep-learning for predicting Differential gene expression from histone modifications
Tool DeepDIff: DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications
BCB- DrQ’s 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.
Rita’s PhD Defense - Fast and Interpretable Classification of Sequential Data in Biology
Ph.D. Dissertation Defense by Ritambhara Singh Monday, April 9, 2018 at 12:00PM in Rice 504.
DrQ Tutorial 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.
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 awar...
NeurIPS - 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
PSB - 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
Bioinformatics - DeepChrome- deep-learning for predicting gene expression from histone modifications
Tool DeepChrome: deep-learning for predicting gene expression from histone modifications
ECML - GaKCo-SVM- a Fast GApped k-mer string Kernel using COunting
Tool GaKCo-SVM: a Fast GApped k-mer string Kernel using COunting
TCBB - TSK- Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Tool TSK: Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
AAAI - 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
ECIR - 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
DrQ 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.
Plos- A unified multitask architecture for predicting local structural properties on proteins
Tool Multitask-ProteinTagging: A unified multitask architecture for predicting local protein properties
AItrust
Dr Qi’s Invited Talks on textattack
On June 24th, 2021, I gave an invited talk at the Science Academy Machine Learning Summer School on “TextAttack: Generalizing Adversarial Examples to Natural...
EMNLP - Benchmarking Search Algorithms for Generating NLP Adversarial Examples
Title: Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples
EMNLP- On Quality of Generated Adversarial Examples and How to Set Attack Contraints
Title: Reevaluating Adversarial Examples in Natural Language
Weilin’s PhD Defense - Improving Robustness of Machine Learning Models using Domain Knowledge
Ph.D. Dissertation Defense by Weilin Xu
DrQi’s 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
DrQi’s 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)...
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).
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 awar...
PSB - 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
VizSec - Adversarial-Playground- A Visualization Suite for Adversarial Sample Generation
Paper Arxiv
AIfastConnectome
CVPR - General Multi-label Image Classification with Transformers
Title: General Multi-label Image Classification with Transformers
DrQi’s 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.
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 Knowle...
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
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:
ICML - JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Tool JEEK: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
AISTAT - 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
NeurIPS - W-SIMULE
Tool W-SIMULE: A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs with Additional Prior knowled...
AISTAT - FASJEM R package is released!
Tool FASJEM: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Journal Machine Learning - SIMULE R package is released!
Tool SIMULE: A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
NeurIPS - 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