Search Results for author: Yang shen

Found 23 papers, 12 papers with code

Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations

1 code implementation4 Jan 2022 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation.

Contrastive Learning Graph Learning

A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval

no code implementations NeurIPS 2021 Xiu-Shen Wei, Yang shen, Xuhao Sun, Han-Jia Ye, Jian Yang

Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations.

Image Retrieval

Mean-Variance Portfolio Selection in Contagious Markets

no code implementations18 Oct 2021 Yang shen, Bin Zou

We consider a mean-variance portfolio selection problem in a financial market with contagion risk.

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

no code implementations ICLR 2022 Yuning You, Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Variational Inference

Algorithmic insights on continual learning from fruit flies

1 code implementation15 Jul 2021 Yang shen, Sanjoy Dasgupta, Saket Navlakha

We discovered a two layer neural circuit in the fruit fly olfactory system that addresses this challenge by uniquely combining sparse coding and associative learning.

Continual Learning

Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

1 code implementation24 Jun 2021 Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang shen

Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.

Graph Contrastive Learning Automated

2 code implementations10 Jun 2021 Yuning You, Tianlong Chen, Yang shen, Zhangyang Wang

Unfortunately, unlike its counterpart on image data, the effectiveness of GraphCL hinges on ad-hoc data augmentations, which have to be manually picked per dataset, by either rules of thumb or trial-and-errors, owing to the diverse nature of graph data.

Contrastive Learning Representation Learning +1

Mean-Variance Investment and Risk Control Strategies -- A Time-Consistent Approach via A Forward Auxiliary Process

no code implementations11 Jan 2021 Yang shen, Bin Zou

By introducing a deterministic auxiliary process defined forward in time, we formulate an alternative time-consistent problem related to the original MV problem, and obtain the optimal strategy and the value function to the new problem in closed-form.

Bayesian Learning to Optimize: Quantifying the Optimizer Uncertainty

no code implementations1 Jan 2021 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Optimizing an objective function with uncertainty awareness is well-known to improve the accuracy and confidence of optimization solutions.

Image Classification Variational Inference

Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

no code implementations14 Nov 2020 Yuning You, Yang shen

Compound-protein pairs dominate FDA-approved drug-target pairs and the prediction of compound-protein affinity and contact (CPAC) could help accelerate drug discovery.

Drug Discovery

Graph Contrastive Learning with Augmentations

2 code implementations NeurIPS 2020 Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang shen

In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data.

Contrastive Learning Representation Learning +2

When Does Self-Supervision Help Graph Convolutional Networks?

1 code implementation ICML 2020 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning.

Multi-Task Learning Representation Learning +1

Network-principled deep generative models for designing drug combinations as graph sets

1 code implementation16 Apr 2020 Mostafa Karimi, Arman Hasanzadeh, Yang shen

We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively training a reinforcement learning-based chemical graph-set designer.

Graph Embedding

L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

2 code implementations CVPR 2020 Yuning You, Tianlong Chen, Zhangyang Wang, Yang shen

Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets.

Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts

no code implementations29 Dec 2019 Mostafa Karimi, Di wu, Zhangyang Wang, Yang shen

DeepRelations shows superior interpretability to the state-of-the-art: without compromising affinity prediction, it boosts the AUPRC of contact prediction 9. 5, 16. 9, 19. 3 and 5. 7-fold for the test, compound-unique, protein-unique, and both-unique sets, respectively.

Drug Discovery Interpretable Machine Learning

Energy-based Graph Convolutional Networks for Scoring Protein Docking Models

no code implementations28 Dec 2019 Yue Cao, Yang shen

Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking.

Learning to Optimize in Swarms

1 code implementation NeurIPS 2019 Yue Cao, Tianlong Chen, Zhangyang Wang, Yang shen

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks.

Bayesian active learning for optimization and uncertainty quantification in protein docking

1 code implementation31 Jan 2019 Yue Cao, Yang shen

To the best of our knowledge, this study represents the first uncertainty quantification solution for protein docking, with theoretical rigor and comprehensive assessment.

Active Learning

Egocentric Activity Prediction via Event Modulated Attention

no code implementations ECCV 2018 Yang Shen, Bingbing Ni, Zefan Li, Ning Zhuang

Predicting future activities from an egocentric viewpoint is of particular interest in assisted living.

Activity Prediction Event Extraction

Learning Correspondence Structures for Person Re-identification

no code implementations20 Mar 2017 Weiyao Lin, Yang shen, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang, Ke Lu

We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.

Patch Matching Person Re-Identification

Person Re-identification with Correspondence Structure Learning

1 code implementation ICCV 2015 Yang Shen, Weiyao Lin, Junchi Yan, Mingliang Xu, Jianxin Wu, Jingdong Wang

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.

Patch Matching Person Re-Identification

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