Наши работы

Наши принципы

Наши работы

Направления исследований

OMUT ai в тг

Omut AI Лаборатория ИИ
→
Наши работы

Наши работы

Omut AI наследует исследовательскую команду T-Bank Research, в которой мы занимаемся созданием новых алгоритмов в AI

2024

2023

2022

Viacheslav Sinii
Alexander Nikulin
Vladislav Kurenkov
Ilya Zisman
Sergey Kolesnikov

In-Context Reinforcement Learning for Variable Action Spaces

ICML 2024

Подробнее

Stanislav Dereka
Ivan Karpukhin
Maksim Zhdanov
Sergey Kolesnikov

Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy

IEEE ICIP 2024

Подробнее

Yaroslav Aksenov
Nikita Balagansky
Sofia Maria Lo Cicero Vaina
Boris Shaposhnikov
Alexey Gorbatovski
Daniil Gavrilov

Linear Transformers with Learnable Kernel Functions are Better In-Context Models

ACL 2024

Подробнее

Ilya Zisman
Vladislav Kurenkov
Alexander Nikulin
Viacheslav Sinii
Sergey Kolesnikov

Emergence of In-Context Reinforcement Learning from Noise Distillation

ICML 2024

Подробнее

Alexey Gorbatovski
Boris Shaposhnikov
Alexey Malakhov
Nikita Surnachev
Yaroslav Aksenov
Ian Maksimov
Nikita Balagansky
Daniil Gavrilov

Learn Your Reference Model for Real Good Alignment

Preprint

Подробнее

Denis Tarasov
Vladislav Kurenkov
Alexander Nikulin
Sergey Kolesnikov

Revisiting the Minimalist Approach to Offline Reinforcement Learning

NeurIPS 2023

Подробнее

Denis Tarasov
Alexander Nikulin
Dmitry Akimov
Vladislav Kurenkov
Sergey Kolesnikov

CORL: Research-oriented Deep Offline Reinforcement Learning Library

NeurIPS 2023

Подробнее

Vladislav Kurenkov
Alexander Nikulin
Denis Tarasov
Sergey Kolesnikov

Katakomba: Tools and Benchmarks for Data-Driven NetHack

NeurIPS 2023

Подробнее

Maksim Zhdanov
Ivan Karpukhin

Catching Image Retrieval Generalization

Preprint

Подробнее

Alexander Nikulin
Vladislav Kurenkov
Denis Tarasov
Sergey Kolesnikov

Anti-Exploration by Random Network Distillation

ICML 2023

Подробнее

Sofia Maria Lo Cicero Vaina
Nikita Balagansky
Daniil Gavrilov

Diffusion Language Models Generation Can Be Halted Early

Preprint

Подробнее

Daniil Gavrilov
Nikita Balagansky

Ahead-of-Time P-Tuning

Preprint

Подробнее

Alexander Nikulin
Vladislav Kurenkov
Ilya Zisman
Artem Agarkov
Viacheslav Sinii
Sergey Kolesnikov

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

NeurIPS 2023

Подробнее

Intrinsically Motivated Open-ended Learning Workshop

Vladislav Kurenkov
Sergey Kolesnikov

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

ICML 2022

Подробнее

Nikita Balagansky
Daniil Gavrilov

PALBERT: Teaching ALBERT to Ponder

NeurIPS 2022

Подробнее

Ivan Karpukhin
Stanislav Dereka
Sergey Kolesnikov

Probabilistic Embeddings Revisited

Preprint

Подробнее

Mark Rofin
Nikita Balagansky
Daniil Gavrilov

Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models

Preprint

Подробнее

Stanislav Dereka
Ivan Karpukhin
Sergey Kolesnikov

Deep Image Retrieval is not Robust to Label Noise

CVPR 2022

Подробнее

Open-Domain Reasoning Under Multi-Modal Settings Workshop

Denis Tarasov
Vladislav Kurenkov
Sergey Kolesnikov

Prompts and Pre-Trained Language Models for Offline Reinforcement Learning

ICLR 2022

Подробнее

Workshop on Generalizable Policy Learning in Physical World

Askhat Sitdikov
Nikita Balagansky
Daniil Gavrilov
Alexander Markov

Classifiers are Better Experts for Controllable Text Generation

ACL 2022

Подробнее

Workshop on Transfer Learning for Natural Language Processing

Ivan Karpukhin
Stanislav Dereka
Sergey Kolesnikov

EXACT: How to Train Your Accuracy

ICML 2022

Подробнее

Topology, Algebra, and Geometry in Machine Learning Workshop

Alexander Nikulin
Vladislav Kurenkov
Denis Tarasov
Dmitry Akimov
Sergey Kolesnikov

Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

NeurIPS 2022

Подробнее

Offline RL Workshop

Dmitriy Akimov
Vladislav Kurenkov
Alexander Nikulin
Denis Tarasov
Sergey Kolesnikov

Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

NeurIPS 2022

Подробнее

Offline RL Workshop

Наши работы

Omut AI наследует исследовательскую команду T-Bank Research, в которой мы занимаемся созданием новых алгоритмов в AI

2024

2023

2022

Viacheslav Sinii
Alexander Nikulin
Vladislav Kurenkov
Ilya Zisman
Sergey Kolesnikov

In-Context Reinforcement Learning for Variable Action Spaces

ICML 2024

Подробнее

Stanislav Dereka
Ivan Karpukhin
Maksim Zhdanov
Sergey Kolesnikov

Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy

IEEE ICIP 2024

Подробнее

Yaroslav Aksenov
Nikita Balagansky
Sofia Maria Lo Cicero Vaina
Boris Shaposhnikov
Alexey Gorbatovski
Daniil Gavrilov

Linear Transformers with Learnable Kernel Functions are Better
In-Context Models

ACL 2024

Подробнее

Ilya Zisman
Vladislav Kurenkov
Alexander Nikulin
Viacheslav Sinii
Sergey Kolesnikov

Emergence of In-Context Reinforcement Learning from Noise Distillation

ICML 2024

Подробнее

Alexey Gorbatovski
Boris Shaposhnikov
Alexey Malakhov
Nikita Surnachev
Yaroslav Aksenov
Ian Maksimov
Nikita Balagansky
Daniil Gavrilov

Learn Your Reference Model for Real Good Alignment

Preprint

Подробнее

Denis Tarasov
Vladislav Kurenkov
Alexander Nikulin
Sergey Kolesnikov

Revisiting the Minimalist Approach to Offline Reinforcement Learning

NeurIPS 2023

Подробнее

Denis Tarasov
Alexander Nikulin
Dmitry Akimov
Vladislav Kurenkov
Sergey Kolesnikov

CORL: Research-oriented Deep Offline Reinforcement Learning Library

NeurIPS 2023

Подробнее

Vladislav Kurenkov
Alexander Nikulin
Denis Tarasov
Sergey Kolesnikov

Katakomba: Tools and Benchmarks for Data-Driven NetHack

NeurIPS 2023

Подробнее

Maksim Zhdanov
Ivan Karpukhin

Catching Image Retrieval Generalization

Preprint

Подробнее

Alexander Nikulin
Vladislav Kurenkov
Ilya Zisman
Artem Agarkov
Viacheslav Sinii
Sergey Kolesnikov

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

NeurIPS 2023

Подробнее

Intrinsically Motivated Open-ended Learning Workshop

Alexander Nikulin
Vladislav Kurenkov
Denis Tarasov
Sergey Kolesnikov

Anti-Exploration by Random Network Distillation

ICML 2023

Подробнее

Sofia Maria Lo Cicero Vaina
Nikita Balagansky
Daniil Gavrilov

Diffusion Language Models Generation Can Be Halted Early

Preprint

Подробнее

Daniil Gavrilov
Nikita Balagansky

Ahead-of-Time P-Tuning

Preprint

Подробнее

Vladislav Kurenkov
Sergey Kolesnikov

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

ICML 2022

Подробнее

Stanislav Dereka
Ivan Karpukhin
Sergey Kolesnikov

Deep Image Retrieval is not Robust to Label Noise

CVPR 2022

Подробнее

Open-Domain Reasoning Under Multi-Modal Settings Workshop

Nikita Balagansky
Daniil Gavrilov

PALBERT: Teaching ALBERT to Ponder

NeurIPS 2022

Подробнее

Denis Tarasov
Vladislav Kurenkov
Sergey Kolesnikov

Prompts and Pre-Trained Language Models for Offline Reinforcement Learning

ICLR 2022

Подробнее

Workshop on Generalizable Policy Learning in Physical World

Askhat Sitdikov
Nikita Balagansky
Daniil Gavrilov
Alexander Markov

Classifiers are Better Experts for Controllable Text Generation

ACL 2022

Подробнее

Workshop on Transfer Learning for Natural Language Processing

Ivan Karpukhin
Stanislav Dereka
Sergey Kolesnikov

EXACT: How to Train Your Accuracy

ICML 2022

Подробнее

Topology, Algebra, and Geometry in Machine Learning Workshop

Alexander Nikulin
Vladislav Kurenkov
Denis Tarasov
Dmitry Akimov
Sergey Kolesnikov

Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

NeurIPS 2022

Подробнее

Offline RL Workshop

Dmitriy Akimov
Vladislav Kurenkov
Alexander Nikulin
Denis Tarasov
Sergey Kolesnikov

Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

NeurIPS 2022

Подробнее

Offline RL Workshop

Ivan Karpukhin
Stanislav Dereka
Sergey Kolesnikov

Probabilistic Embeddings Revisited

Preprint

Подробнее

Mark Rofin
Nikita Balagansky
Daniil Gavrilov

Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models

Preprint

Подробнее

Вернуться на главную

Образовательные услуги оказываются АНО ВО «Центральный университет», свидетельство № А007−115−77/1 039 609, 29 января 2024

Политика обработки персональных данных

Абитуриентам

Бакалавриат

Магистратура

apply@centraluniversity.ru

+7 (800) 511 25 51

Дополнительное образование

Мероприятия

Новости

Университет

контакты

Контакты

Образовательный кредит

Сведения об организации

Преподаватели

Противодействие коррупции

@HELLO_CENTRALUNIVERSITY_BOT

Чат с командой Центрального университета. Здесь ты можешь задать любые вопросы о поступлении и учебе у нас

Оплата услуг

Наши работы

Omut AI наследует исследовательскую команду T-Bank Research, в которой мы занимаемся созданием новых алгоритмов в AI

In-Context Reinforcement Learning for Variable Action Spaces

Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy

Linear Transformers with Learnable Kernel Functions are Better In-Context Models

Emergence of In-Context Reinforcement Learning from Noise Distillation

Learn Your Reference Model for Real Good Alignment

Revisiting the Minimalist Approach to Offline Reinforcement Learning

CORL: Research-oriented Deep Offline Reinforcement Learning Library

Katakomba: Tools and Benchmarks for Data-Driven NetHack

Catching Image Retrieval Generalization

Anti-Exploration by Random Network Distillation

Diffusion Language Models Generation Can Be Halted Early

Ahead-of-Time P-Tuning

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

PALBERT: Teaching ALBERT to Ponder

Probabilistic Embeddings Revisited

Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models

Deep Image Retrieval is not Robust to Label Noise

Prompts and Pre-Trained Language Models for Offline Reinforcement Learning

Classifiers are Better Experts for Controllable Text Generation

EXACT: How to Train Your Accuracy

Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Наши работы

Omut AI наследует исследовательскую команду T-Bank Research, в которой мы занимаемся созданием новых алгоритмов в AI

In-Context Reinforcement Learning for Variable Action Spaces

Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and Accuracy

Linear Transformers with Learnable Kernel Functions are BetterIn-Context Models

Emergence of In-Context Reinforcement Learning from Noise Distillation

Learn Your Reference Model for Real Good Alignment

Revisiting the Minimalist Approach to Offline Reinforcement Learning

CORL: Research-oriented Deep Offline Reinforcement Learning Library

Katakomba: Tools and Benchmarks for Data-Driven NetHack

Catching Image Retrieval Generalization

XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX

Anti-Exploration by Random Network Distillation

Diffusion Language Models Generation Can Be Halted Early

Ahead-of-Time P-Tuning

Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters

Deep Image Retrieval is not Robust to Label Noise

PALBERT: Teaching ALBERT to Ponder

Prompts and Pre-Trained Language Models for Offline Reinforcement Learning

Classifiers are Better Experts for Controllable Text Generation

EXACT: How to Train Your Accuracy

Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows

Probabilistic Embeddings Revisited

Linear Interpolation In Parameter Space is Good Enough for Fine-Tuned Language Models

Linear Transformers with Learnable Kernel Functions are Better
In-Context Models