Correlational Lagrangian Schrödinger Bridge: Learning Dynamics with Population-Level Regularization

no code implementations • 4 Feb 2024 • Yuning You, Ruida Zhou, Yang shen

Accurate modeling of system dynamics holds intriguing potential in broad scientific fields including cytodynamics and fluid mechanics.

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

1 code implementation • 7 Oct 2022 • Tianxin Wei, Yuning You, Tianlong Chen, Yang shen, Jingrui He, Zhangyang Wang

This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).

Contrastive Learning Fairness +1

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

1 code implementation • 4 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

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.

Uncertainty Quantification Variational Inference

Graph Contrastive Learning Automated

2 code implementations • 10 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

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

no code implementations • 14 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

4 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

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.