1 code implementation • 6 Jan 2024 • Igor Udovichenko, Egor Shvetsov, Denis Divitsky, Dmitry Osin, Ilya Trofimov, Anatoly Glushenko, Ivan Sukharev, Dmitry Berestenev, Evgeny Burnaev
As a result of our work we demonstrate that our method surpasses state of the art NAS methods and popular architectures suitable for sequence classification and holds great potential for various industrial applications.
1 code implementation • 24 Aug 2023 • Nikita Balabin, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov
We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding a multi-scale topological loss term.
1 code implementation • 31 Jan 2023 • Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii, Nikita Balabin, Evgeny Burnaev, Serguei Barannikov
The method aims to provide topological similarity between the data manifold and its latent representation via enforcing the similarity in topological features (clusters, loops, 2D voids, etc.)
no code implementations • 17 Oct 2022 • Timofey Grigoryev, Polina Verezemskaya, Mikhail Krinitskiy, Nikita Anikin, Alexander Gavrikov, Ilya Trofimov, Nikita Balabin, Aleksei Shpilman, Andrei Eremchenko, Sergey Gulev, Evgeny Burnaev, Vladimir Vanovskiy
Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts to make them safe.
2 code implementations • 31 Aug 2022 • Egor Shvetsov, Dmitry Osin, Alexey Zaytsev, Ivan Koryakovskiy, Valentin Buchnev, Ilya Trofimov, Evgeny Burnaev
The approach utilizes entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure.
1 code implementation • 31 Dec 2021 • Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev
Comparison of data representations is a complex multi-aspect problem that has not enjoyed a complete solution yet.
no code implementations • 29 Sep 2021 • Serguei Barannikov, Ilya Trofimov, Nikita Balabin, Evgeny Burnaev
We propose a method for comparing two data representations.
2 code implementations • NeurIPS 2021 • Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev
We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
1 code implementation • NeurIPS 2021 • Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov, Ekaterina Trimbach, Alexander Korotin, Alexander Filippov, Evgeny Burnaev
We propose a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models.
no code implementations • 31 Dec 2020 • Serguei Barannikov, Daria Voronkova, Ilya Trofimov, Alexander Korotin, Grigorii Sotnikov, Evgeny Burnaev
We define the neural network Topological Obstructions score, "TO-score", with the help of robust topological invariants, barcodes of the loss function, that quantify the "badness" of local minima for gradient-based optimization.
1 code implementation • 15 Jun 2020 • Ilya Trofimov, Nikita Klyuchnikov, Mikhail Salnikov, Alexander Filippov, Evgeny Burnaev
The method relies on a new approach to low-fidelity evaluations of neural architectures by training for a few epochs using a knowledge distillation.
1 code implementation • 12 Jun 2020 • Nikita Klyuchnikov, Ilya Trofimov, Ekaterina Artemova, Mikhail Salnikov, Maxim Fedorov, Evgeny Burnaev
In this work, we step outside the computer vision domain by leveraging the language modeling task, which is the core of natural language processing (NLP).
1 code implementation • 25 Sep 2018 • Ilya Trofimov
These vector representations are used for making complementary products recommendation.
1 code implementation • 7 Nov 2016 • Ilya Trofimov, Alexander Genkin
Generalized linear model with $L_1$ and $L_2$ regularization is a widely used technique for solving classification, class probability estimation and regression problems.
no code implementations • 20 Dec 2014 • Afroze Ibrahim Baqapuri, Ilya Trofimov
Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE).
1 code implementation • 24 Nov 2014 • Ilya Trofimov, Alexander Genkin
Solving logistic regression with L1-regularization in distributed settings is an important problem.