Zobrazeno 1 - 10
of 577
pro vyhledávání: '"A. A. Barannikov"'
Publikováno v:
Финансы: теория и практика, Vol 28, Iss 4, Pp 84-96 (2024)
The stability of the national economy is dependent on the investment climate. The purpose of the study is to identify prospects for improving the investment climate in Russia by taking into account the level of influence of investments in individual
Externí odkaz:
https://doaj.org/article/e8c2272bdb214c4587faff21839164d5
Autor:
Kuznetsov, Kristian, Tulchinskii, Eduard, Kushnareva, Laida, Magai, German, Barannikov, Serguei, Nikolenko, Sergey, Piontkovskaya, Irina
Growing amount and quality of AI-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advance. In this work, we focus on the
Externí odkaz:
http://arxiv.org/abs/2410.08113
Autor:
Tulchinskii, Eduard, Kushnareva, Laida, Kuznetsov, Kristian, Voznyuk, Anastasia, Andriiainen, Andrei, Piontkovskaya, Irina, Burnaev, Evgeny, Barannikov, Serguei
A standard way to evaluate the abilities of LLM involves presenting a multiple-choice question and selecting the option with the highest logit as the model's predicted answer. However, such a format for evaluating LLMs has limitations, since even if
Externí odkaz:
http://arxiv.org/abs/2410.02343
Publikováno v:
ECCV 2024
We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domain. Functions can be defined on an
Externí odkaz:
http://arxiv.org/abs/2407.08364
Autor:
Gaintseva, Tatiana, Kushnareva, Laida, Magai, German, Piontkovskaya, Irina, Nikolenko, Sergey, Benning, Martin, Barannikov, Serguei, Slabaugh, Gregory
With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suffer from poor generalization across domains and generative models. In t
Externí odkaz:
http://arxiv.org/abs/2406.15035
Autor:
Shumilin, Sergei, Ryabov, Alexander, Barannikov, Serguei, Burnaev, Evgeny, Vanovskii, Vladimir
Voronoi tessellation, also known as Voronoi diagram, is an important computational geometry technique that has applications in various scientific disciplines. It involves dividing a given space into regions based on the proximity to a set of points.
Externí odkaz:
http://arxiv.org/abs/2312.16192
Autor:
Kushnareva, Laida, Gaintseva, Tatiana, Magai, German, Barannikov, Serguei, Abulkhanov, Dmitry, Kuznetsov, Kristian, Tulchinskii, Eduard, Piontkovskaya, Irina, Nikolenko, Sergey
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated. Detecting the boundary between human-written and machine-generated parts of such
Externí odkaz:
http://arxiv.org/abs/2311.08349
Publikováno v:
ICML 2024, Proceedings of the 41st International Conference on Machine Learning
We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding a multi-scale topological loss term. Disentanglement is a crucial property of data representations substantial for the explainability and r
Externí odkaz:
http://arxiv.org/abs/2308.12696
Autor:
Tulchinskii, Eduard, Kuznetsov, Kristian, Kushnareva, Laida, Cherniavskii, Daniil, Barannikov, Serguei, Piontkovskaya, Irina, Nikolenko, Sergey, Burnaev, Evgeny
Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of
Externí odkaz:
http://arxiv.org/abs/2306.04723
Autor:
Trofimov, Ilya, Cherniavskii, Daniil, Tulchinskii, Eduard, Balabin, Nikita, Burnaev, Evgeny, Barannikov, Serguei
Publikováno v:
11th International Conference on Learning Representations (ICLR 2023)
We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topologica
Externí odkaz:
http://arxiv.org/abs/2302.00136