19 citations · 29 across the 10 of their papers we have counts for
5 papers · 1 filter
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose +9
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem…
Improved off-policy training of diffusion samplers
Marcin Sendera, Minsu Kim, Sarthak Mittal +6
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark several diffusion-structured infe…
Improving Gradient-guided Nested Sampling for Posterior Inference
Pablo Lemos, Nikolay Malkin, Will Handley +3
We present a performant, general-purpose gradient-guided nested sampling algorithm, , combining the state of the art in differentiable programming, Hamiltonian slice sa…
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova, Michał Koziarski, Alex Hernández-García +7
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampl…
A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos +6
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A…