5 papers
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…
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…
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…