9 citations · 48 across the 21 of their papers we have counts for
21 papers
Time-Aware Item Weighting for the Next Basket Recommendations
Aleksey Romanov, Oleg Lashinin, Marina Ananyeva +1
In this paper we study the next basket recommendation problem. Recent methods use different approaches to achieve better performance. However, many of them do not use information a…
RecBaselines2023: a new dataset for choosing baselines for recommender models
Veronika Ivanova, Oleg Lashinin, Marina Ananyeva +1
The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number…
Katakomba: Tools and Benchmarks for Data-Driven NetHack
Vladislav Kurenkov, Alexander Nikulin, Denis Tarasov +1
NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions. One of the promising directions for…
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy
Stanislav Dereka, Ivan Karpukhin, Maksim Zhdanov +1
Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection. However, their effectiveness is limited due to the homog…
Revisiting the Minimalist Approach to Offline Reinforcement Learning
Denis Tarasov, Vladislav Kurenkov, Alexander Nikulin +1
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.…
Anti-Exploration by Random Network Distillation
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov +1
Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-…
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
Dmitriy Akimov, Vladislav Kurenkov, Alexander Nikulin +2
Offline reinforcement learning aims to train a policy on a pre-recorded and fixed dataset without any additional environment interactions. There are two major challenges in this se…
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Alexander Nikulin, Vladislav Kurenkov, Denis Tarasov +2
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks. To address this problem, large-batch optimization was introduced…
CORL: Research-oriented Deep Offline Reinforcement Learning Library
Denis Tarasov, Alexander Nikulin, Dmitry Akimov +2
CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emp…
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…
EXACT: How to Train Your Accuracy
Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov
Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize…
CVTT: Cross-Validation Through Time
Mikhail Andronov, Sergey Kolesnikov
The evaluation of recommender systems from a practical perspective is a topic of ongoing discourse within the research community. While many current evaluation methods reduce perfo…
Probabilistic Embeddings Revisited
Ivan Karpukhin, Stanislav Dereka, Sergey Kolesnikov
In recent years, deep metric learning and its probabilistic extensions claimed state-of-the-art results in the face verification task. Despite improvements in face verification, pr…
Next Period Recommendation Reality Check
Sergey Kolesnikov, Oleg Lashinin, Michail Pechatov +1
Over the past decade, tremendous progress has been made in Recommender Systems (RecSys) for well-known tasks such as next-item and next-basket prediction. On the other hand, the re…
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters
Vladislav Kurenkov, Sergey Kolesnikov
In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms. First, we delineate that the online evaluation bud…
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…
Run, skeleton, run: skeletal model in a physics-based simulation
Mikhail Pavlov, Sergey Kolesnikov, Sergey M. Plis
In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navi…