1 code implementation • ACL 2022 • Artem Vazhentsev, Gleb Kuzmin, Artem Shelmanov, Akim Tsvigun, Evgenii Tsymbalov, Kirill Fedyanin, Maxim Panov, Alexander Panchenko, Gleb Gusev, Mikhail Burtsev, Manvel Avetisian, Leonid Zhukov
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc.
no code implementations • 13 Nov 2023 • Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov
Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields.
no code implementations • 27 Apr 2023 • Mastane Achab, REDA ALAMI, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return.
Distributional Reinforcement Learning reinforcement-learning +1
1 code implementation • 5 Sep 2022 • Roman Kail, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, Maxim Panov
The performance of modern deep learning-based systems dramatically depends on the quality of input objects.
1 code implementation • 7 Feb 2022 • Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, Maxim Panov
This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.
1 code implementation • EACL 2021 • Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov
In this work, we consider the problem of uncertainty estimation for Transformer-based models.
no code implementations • 30 Sep 2020 • Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin, Maxim Panov, Dmitry Berestnev
In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients.
1 code implementation • 6 Mar 2020 • Kirill Fedyanin, Evgenii Tsymbalov, Maxim Panov
Uncertainty estimation for machine learning models is of high importance in many scenarios such as constructing the confidence intervals for model predictions and detection of out-of-distribution or adversarially generated points.
no code implementations • 23 Jan 2020 • Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev, Maxim Panov
Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time.