58 citations · 61 across the 7 of their papers we have counts for
7 papers
When Riemann flows with Wasserstein: Generative Modeling of Probability Distributions on Manifolds
Doron Haviv, Edward De Brouwer, Rishabh Anand +4
Many scientific datasets, such as molecular conformational ensembles or single-cell tissue measurements, are naturally modeled as meta-distributions: distributions over probability…
scCBGM: Interpretable Single-Cell Counterfactual Editing
Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer +6
Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequencing enables characterization…
Wasserstein Flow Matching: Generative modeling over families of distributions
Doron Haviv, Aram-Alexandre Pooladian, Dana Pe'er +1
Generative modeling typically concerns transporting a single source distribution to a target distribution via simple probability flows. However, in fields like computer graphics an…
Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers
Doron Haviv, Russell Zhang Kunes, Thomas Dougherty +4
Optimal transport (OT) and the related Wasserstein metric (W) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapid…
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping
Russell Z. Kunes, Mingzhang Yin, Max Land +3
Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) tr…
The Ramanujan Machine: Automatically Generated Conjectures on Fundamental Constants
Gal Raayoni, Shahar Gottlieb, George Pisha +6
Fundamental mathematical constants like and are ubiquitous in diverse fields of science, from abstract mathematics to physics, biology and chemistry. For centuries, new for…
Understanding and Controlling Memory in Recurrent Neural Networks
Doron Haviv, Alexander Rivkind, Omri Barak
To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories…