1k citations · 3k across the 26 of their papers we have counts for
26 papers
Introduction to Stochastic Differential Equations for Generative Machine Learning: A Variational Perspective
Ole Winther, Paul Jeha, Sander Dieleman +3
The use of ordinary and stochastic differential equations has led to substantial progress in generative machine learning with applications to, for example, image, video and biomole…
Uncertainty evaluation of segmentation models for Earth observation
Melanie Rey, Andriy Mnih, Maxim Neumann +2
This paper investigates methods for estimating uncertainty in semantic segmentation predictions derived from satellite imagery. Estimating uncertainty for segmentation presents uni…
Schrödinger Bridge Flow for Unpaired Data Translation
Valentin De Bortoli, Iryna Korshunova, Andriy Mnih +1
Mass transport problems arise in many areas of machine learning whereby one wants to compute a map transporting one distribution to another. Generative modeling techniques like Gen…
Compositional Score Modeling for Simulation-based Inference
Tomas Geffner, George Papamakarios, Andriy Mnih
Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as…
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts
Wouter Kool, Chris J. Maddison, Andriy Mnih
Training large-scale mixture of experts models efficiently on modern hardware requires assigning datapoints in a batch to different experts, each with a limited capacity. Recently…
Coupled Gradient Estimators for Discrete Latent Variables
Zhe Dong, Andriy Mnih, George Tucker
Training models with discrete latent variables is challenging due to the high variance of unbiased gradient estimators. While low-variance reparameterization gradients of a continu…