3 papers
cs.LG2026
Distribution-Conditioned Transport
Nic Fishman, Gokul Gowri, Paolo L. B. Fischer +3
Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require mod…
cs.LG2026
Count Bridges enable Modeling and Deconvolving Transcriptomic Data
Nic Fishman, Gokul Gowri, Tanush Kumar +4
Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired…
cs.LG2026
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…