2 citations · 2 across the 5 of their papers we have counts for
5 papers
Adaptive Destruction Processes for Diffusion Samplers
Timofei Gritsaev, Nikita Morozov, Kirill Tamogashev +5
This paper explores the challenges and benefits of a trainable destruction process in diffusion samplers -- diffusion-based generative models trained to sample an unnormalised dens…
Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation
Denis Rakitin, Ivan Shchekotov, Dmitry Vetrov
Diffusion distillation methods aim to compress the diffusion models into efficient one-step generators while trying to preserve quality. Among them, Distribution Matching Distillat…
Improving GFlowNets with Monte Carlo Tree Search
Nikita Morozov, Daniil Tiapkin, Sergey Samsonov +2
Generative Flow Networks (GFlowNets) treat sampling from distributions over compositional discrete spaces as a sequential decision-making problem, training a stochastic policy to c…
Gradual Optimization Learning for Conformational Energy Minimization
Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov +7
Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization meth…
Generative Flow Networks as Entropy-Regularized RL
Daniil Tiapkin, Nikita Morozov, Alexey Naumov +1
The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given rew…