35 citations · 109 across the 9 of their papers we have counts for
9 papers
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
Yuanfeng Ji, Lu Zhang, Jiaxiang Wu +16
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its…
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
Yatao Bian, Yu Rong, Tingyang Xu +3
Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such…
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models
Jiaxiang Wu, Shitong Luo, Tao Shen +3
Accurate protein structure prediction from amino-acid sequences is critical to better understanding the protein function. Recent advances in this area largely benefit from more pre…
tFold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy for Template-Based Modeling Structure Refinement
Liangzhen Zheng, Haidong Lan, Tao Shen +4
Protein structure prediction has been a grand challenge for over 50 years, owing to its broad scientific and application interests. There are two primary types of modeling algorith…
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation
Peng Sun, Jiaxiang Wu, Songyuan Li +3
To satisfy the stringent requirements on computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight s…
Exploring Fast and Communication-Efficient Algorithms in Large-scale Distributed Networks
Yue Yu, Jiaxiang Wu, Junzhou Huang
The communication overhead has become a significant bottleneck in data-parallel network with the increasing of model size and data samples. In this work, we propose a new algorithm…
An Efficient Approach to Informative Feature Extraction from Multimodal Data
Lichen Wang, Jiaxiang Wu, Shao-Lun Huang +4
One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the…
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Jiaxiang Wu, Weidong Huang, Junzhou Huang +1
Large-scale distributed optimization is of great importance in various applications. For data-parallel based distributed learning, the inter-node gradient communication often becom…
Double Quantization for Communication-Efficient Distributed Optimization
Yue Yu, Jiaxiang Wu, Longbo Huang
Modern distributed training of machine learning models suffers from high communication overhead for synchronizing stochastic gradients and model parameters. In this paper, to reduc…