53 citations · 110 across the 16 of their papers we have counts for
37 papers
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin +2
Structured latent variables allow incorporating meaningful prior knowledge into deep learning models. However, learning with such variables remains challenging because of their dis…
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
Pavel Andreev, Alexander Fritzler, Dmitry Vetrov
Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photo-realistic digital avatars, semantic content editing, and quality e…
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Arsenii Ashukha, Andrei Atanov, Dmitry Vetrov
Averaging predictions over a set of models -- an ensemble -- is widely used to improve predictive performance and uncertainty estimation of deep learning models. At the same time,…
Involutive MCMC: a Unifying Framework
Kirill Neklyudov, Max Welling, Evgenii Egorov +1
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. The field has developed a broad sp…
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov
One of the generally accepted views of modern deep learning is that increasing the number of parameters usually leads to better quality. The two easiest ways to increase the number…
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin +1
The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous co…