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cs.LG2026
Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
Haozhe Jia, Pengyu Yin, Wenshuo Chen +6
Physics-informed diffusion models typically enforce PDE constraints only on final outputs, leaving intermediate representations unconstrained and prone to shortcut learning under s…
cs.LG2025
Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learning
Nic Fishman, Gokul Gowri, Peng Yin +2
Many real-world problems require reasoning across multiple scales, demanding models which operate not on single data points, but on entire distributions. We introduce generative di…