ACM BCB - Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences
Deep Learning's Generalization, Especially on structured discrete data
This front adapts from our legacy website deeplearning4discrete.net and introduces a suite of
deep learning tools we have developed for improve predictors' generalization, especially when on structured
data types like text, graph, or sets. Please feel free to email me when you find my typos.
Background on why Generalization topics of Deep Learning are interesting?
Generalization refers to how a machine model adapts properly to new, previously unseen data.
Background on Representation Learning and Deep Learning
The performance of machine learning algorithms is largely dependent on the data representation (or features) on which they are applied. Deep learning aims at discovering learning algorithms that can find multiple levels of representations directly from data, with higher levels representing more abstract concepts. In recent years, the field of deep learning has lead to groundbreaking performance in many applications such as computer vision, speech understanding, natural language processing, and computational biology.
Why structured discrete Data is Interesting?
Deep learning constructs networks of parameterized functional modules and is trained from reference examples using gradient-based optimization [Lecun19].
Since it is hard to estimate gradients through functions of discrete random variables, researching on how to make deep learning behave well on discrete structured data and structured representation interests us. Developing such techniques are an active research area. We focus on investigating interpretable and scalable techniques for doing so.
Relevant Papers we published
- Please check out each item in our side-bar
We have focused on weak supervision in multiple of our papers along this line of research.
Contacts:
Have questions or suggestions? Feel free to ask me on Twitter or email me.
Thanks for reading!
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
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