15 citations · 39 across the 12 of their papers we have counts for
4 papers · 1 filter
Improving Generative Flow Networks with Path Regularization
Anh Do, Duy Dinh, Tan Nguyen +3
Generative Flow Networks (GFlowNets) are recently proposed models for learning stochastic policies that generate compositional objects by sequences of actions with the probability…
Heavy Ball Neural Ordinary Differential Equations
Hedi Xia, Vai Suliafu, Hangjie Ji +4
We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural OD…
Wasserstein-based Projections with Applications to Inverse Problems
Howard Heaton, Samy Wu Fung, Alex Tong Lin +2
Inverse problems consist of recovering a signal from a collection of noisy measurements. These are typically cast as optimization problems, with classic approaches using a data fid…
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Bao Wang, Difan Zou, Quanquan Gu +1
As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior samplin…