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cs.LG2025
Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
Yang Cao, Zhao Song, Jiahao Zhang +1
Graph neural networks (GNNs) have become a core paradigm for learning on relational data. In materials science, equivariant GNNs (EGNNs) have emerged as a compelling backbone for c…
cs.LG2025★ 1 cited
T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation
Xuyang Guo, Jiayan Huo, Zhenmei Shi +3
Text-to-video generative models have made significant strides in recent years, producing high-quality videos that excel in both aesthetic appeal and accurate instruction following,…
cs.LG2023
Toward Highly Efficient and Private Submodular Maximization via Matrix-Based Acceleration
Boyu Liu, Lianke Qin, Zhao Song +2
Submodular function maximization is a critical building block for diverse tasks, such as document summarization, sensor placement, and image segmentation. Yet its practical utility…