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cs.LG2026
Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow
Hanru Bai, Yuncheng Zhou, Difan Zou
Deep learning paradigms, such as PINNs and neural operators, have significantly advanced the solving of PDEs. However, they often struggle to capture the continuous integral nature…
cs.LG2025
Hyper-SET: Designing Transformers via Hyperspherical Energy Minimization
Yunzhe Hu, Difan Zou, Dong Xu
Transformer-based models have achieved remarkable success, but their core components, Transformer layers, are largely heuristics-driven and engineered from the bottom up, calling f…
cs.LG2024
An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models
Yunzhe Hu, Difan Zou, Dong Xu
Deep neural networks have long been criticized for being black-box. To unveil the inner workings of modern neural architectures, a recent work \cite{yu2024white} proposed an inform…