6 citations · 19 across the 6 of their papers we have counts for
7 papers
Deep Image Retrieval is not Robust to Label Noise
Stanislav Dereka, Ivan Karpukhin, Sergey Kolesnikov
Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to labe…
LRWR: Large-Scale Benchmark for Lip Reading in Russian language
Evgeniy Egorov, Vasily Kostyumov, Mikhail Konyk +1
Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas. One of the signi…
Sample Efficient Ensemble Learning with Catalyst.RL
Sergey Kolesnikov, Valentin Khrulkov
We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scal…
Catalyst.RL: A Distributed Framework for Reproducible RL Research
Sergey Kolesnikov, Oleksii Hrinchuk
Despite the recent progress in deep reinforcement learning field (RL), and, arguably because of it, a large body of work remains to be done in reproducing and carefully comparing d…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…