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.
1 code implementation • 30 Aug 2023 • Dimitrios Mallis, Sk Aziz Ali, Elona Dupont, Kseniya Cherenkova, Ahmet Serdar Karadeniz, Mohammad Sadil Khan, Anis Kacem, Gleb Gusev, Djamila Aouada
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
no code implementations • 13 Apr 2023 • Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas.
no code implementations • 22 Aug 2022 • Elona Dupont, Kseniya Cherenkova, Anis Kacem, Sk Aziz Ali, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.
1 code implementation • Findings (NAACL) 2022 • Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov
Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models.
1 code implementation • 15 Jun 2021 • Ivan Fursov, Matvey Morozov, Nina Kaploukhaya, Elizaveta Kovtun, Rodrigo Rivera-Castro, Gleb Gusev, Dmitry Babaev, Ivan Kireev, Alexey Zaytsev, Evgeny Burnaev
In this work, we examine adversarial attacks on transaction records data and defences from these attacks.
no code implementations • 12 Jan 2021 • Kseniya Cherenkova, Djamila Aouada, Gleb Gusev
This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models.
no code implementations • 26 Oct 2020 • Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Konstantinos Papadopoulos, Julian Chibane, Gerard Pons-Moll, Gleb Gusev, David Fofi, Djamila Aouada, Bjorn Ottersten
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
3 code implementations • 19 Feb 2020 • Dmitrii Babaev, Ivan Kireev, Nikita Ovsov, Mariya Ivanova, Gleb Gusev, Ivan Nazarov, Alexander Tuzhilin
We address the problem of self-supervised learning on discrete event sequences generated by real-world users.
no code implementations • NeurIPS 2019 • Bulat Ibragimov, Gleb Gusev
Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees.
no code implementations • 20 Jun 2019 • Valentina Fedorova, Gleb Gusev, Pavel Serdyukov
We study the problem of aggregation noisy labels.
no code implementations • 9 Jun 2019 • Nadezhda Bugakova, Valentina Fedorova, Gleb Gusev, Alexey Drutsa
Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous models for aggregation of pairwise comparisons do not focus on modeling such kind of biases.
no code implementations • 4 Aug 2018 • Eman Ahmed, Alexandre Saint, Abd El Rahman Shabayek, Kseniya Cherenkova, Rig Das, Gleb Gusev, Djamila Aouada, Bjorn Ottersten
3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes.
10 code implementations • NeurIPS 2018 • Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, Andrey Gulin
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit.
1 code implementation • ACL 2017 • Alexander Fonarev, Oleksii Hrinchuk, Gleb Gusev, Pavel Serdyukov, Ivan Oseledets
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.
no code implementations • 13 Dec 2016 • Alexey Drutsa, Andrey Shutovich, Philipp Pushnyakov, Evgeniy Krokhalyov, Gleb Gusev, Pavel Serdyukov
We develop a novel approach to build intent-aware user behavior models, which overcome these limitations and convert to quality metrics that better correlate with standard online metrics of user satisfaction.
no code implementations • NeurIPS 2016 • Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov, Gleb Gusev, Yurii Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii
In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges.
1 code implementation • NeurIPS 2016 • Alexander Shishkin, Anastasia Bezzubtseva, Alexey Drutsa, Ilia Shishkov, Ekaterina Gladkikh, Gleb Gusev, Pavel Serdyukov
This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI).
no code implementations • 28 Nov 2016 • Alexey Drutsa, Gleb Gusev, Pavel Serdyukov
We investigate video popularity prediction based on features from three primary sources available for a typical operating company: first, the content hosting provider may deliver its data via its API, second, the operating company makes use of its own search and browsing logs, third, the company crawls information about embeds of a video and links to a video page from publicly available resources on the Web.
no code implementations • 16 Oct 2016 • Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, Ivan Oseledets
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item.
no code implementations • 17 Jul 2015 • Aleksandr Vorobev, Gleb Gusev
We study the stochastic multi-armed bandit problem with non-equivalent multiple plays where, at each step, an agent chooses not only a set of arms, but also their order, which influences reward distribution.