4 papers
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…
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…
A measurement noise scaling law for cellular representation learning
Gokul Gowri, Igor Sadalski, Dan Raviv +3
Large genomic and imaging datasets can be used to train models that learn meaningful representations of cellular systems. Across domains, model performance improves predictably wit…
Approximating mutual information of high-dimensional variables using learned representations
Gokul Gowri, Xiao-Kang Lun, Allon M. Klein +1
Mutual information (MI) is a general measure of statistical dependence with widespread application across the sciences. However, estimating MI between multi-dimensional variables i…