2 citations · 2 across the 3 of their papers we have counts for
3 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…
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…
Weight Averaging Improves Knowledge Distillation under Domain Shift
Valeriy Berezovskiy, Nikita Morozov
Knowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mim…