1 citations · 1 across the 14 of their papers we have counts for
15 papers
Learning Field Reconstruction from Incomplete Data by Globally Correcting Local Estimates
Renhao Zhong, Zihan Zhou, Chiyuan Ma +1
Reconstructing physical fields from training samples that are always incomplete requires learning spatial structure from fragmented observations. Existing context--query work estab…
Learning What to Trust: Bayesian Prior-Guided Optimization for Visual Generation
Ruiying Liu, Yuanzhi Liang, Haibin Huang +2
Group Relative Policy Optimization (GRPO) has emerged as an effective and lightweight framework for post-training visual generative models. However, its performance is fundamentall…
TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensembles
Yaoyao Xu, Di Wang, Zihan Zhou +2
Understanding the dynamic behavior of proteins is critical to elucidating their functional mechanisms, yet generating realistic, temporally coherent trajectories of protein ensembl…
UM3: Unsupervised Map to Map Matching
Chaolong Ying, Yinan Zhang, Lei Zhang +3
Map-to-map matching is a critical task for aligning spatial data across heterogeneous sources, yet it remains challenging due to the lack of ground truth correspondences, sparse no…
Secrets of GFlowNets' Learning Behavior: A Theoretical Study
Tianshu Yu
Generative Flow Networks (GFlowNets) have emerged as a powerful paradigm for generating composite structures, demonstrating considerable promise across diverse applications. While…
Neural Graduated Assignment for Maximum Common Edge Subgraphs
Chaolong Ying, Yingqi Ruan, Xuemin Chen +2
The Maximum Common Edge Subgraph (MCES) problem is a crucial challenge with significant implications in domains such as biology and chemistry. Traditional approaches, which include…
ReMAP: Lossless Residual Neural Lifting for Scalable MAP Inference in Arbitrary-Order Markov Random Fields
Yaomin Wang, Chaolong Ying, Xiaodong Luo +1
Maximum a posteriori inference in a finite Markov random field is generally NP-hard. We study an instance-wise optimizer that minimizes the exact multilinear energy under product m…
Enhancing Graph Self-Supervised Learning with Graph Interplay
Xinjian Zhao, Wei Pang, Xiangru Jian +3
Graph self-supervised learning (GSSL) has emerged as a compelling framework for extracting informative representations from graph-structured data without extensive reliance on labe…
Boosting Protein Language Models with Negative Sample Mining
Yaoyao Xu, Xinjian Zhao, Xiaozhuang Song +2
We introduce a pioneering methodology for boosting large language models in the domain of protein representation learning. Our primary contribution lies in the refinement process f…
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning
Xiangru Jian, Xinjian Zhao, Wei Pang +4
The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. Spectral augmentation, which involves modify…
On Diffusion Process in SE(3)-invariant Space
Zihan Zhou, Ruiying Liu, Jiachen Zheng +2
Sampling viable 3D structures (e.g., molecules and point clouds) with SE(3)-invariance using diffusion-based models proved promising in a variety of real-world applications, wherei…
Graph Learning with Distributional Edge Layouts
Xinjian Zhao, Chaolong Ying, Tianshu Yu
Graph Neural Networks (GNNs) learn from graph-structured data by passing local messages between neighboring nodes along edges on certain topological layouts. Typically, these topol…
Boosting Graph Pooling with Persistent Homology
Chaolong Ying, Xinjian Zhao, Tianshu Yu
Recently, there has been an emerging trend to integrate persistent homology (PH) into graph neural networks (GNNs) to enrich expressive power. However, naively plugging PH features…
Molecular Conformation Generation via Shifting Scores
Zihan Zhou, Ruiying Liu, Chaolong Ying +2
Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule. Generating molecu…
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space
Zihan Zhou, Ruiying Liu, Tianshu Yu
Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic di…