1 citations · 1 across the 14 of their papers we have counts for
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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…