9 citations · 48 across the 21 of their papers we have counts for
7 papers · 1 filter
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…