553 citations · 3.2k across the 83 of their papers we have counts for
17 papers · 1 filter
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models
Meng Liu, Haoran Liu, Shuiwang Ji
Learning energy-based models (EBMs) is known to be difficult especially on discrete data where gradient-based learning strategies cannot be applied directly. Although ratio matchin…
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin +1
We consider representation learning on periodic graphs encoding crystal materials. Different from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on…
Learning Hierarchical Protein Representations via Complete 3D Graph Networks
Limei Wang, Haoran Liu, Yi Liu +2
We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depen…
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions
Rui Yang, Jie Wang, Zijie Geng +4
Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios. However, visual di…
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Haiyang Yu, Limei Wang, Bokun Wang +3
Training of graph neural networks (GNNs) for large-scale node classification is challenging. A key difficulty lies in obtaining accurate hidden node representations while avoiding…
Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
Xueliang Wang, Jianyu Cai, Shuiwang Ji +3
Few-shot classification aims to learn a model that can generalize well to new tasks when only a few labeled samples are available. To make use of unlabeled data that are more abund…