1 code implementation • LREC 2022 • Natalia Loukachevitch, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Suresh Manandhar, Artem Shelmanov, Elena Tutubalina
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
1 code implementation • LREC 2022 • Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko
Medical data annotation requires highly qualified expertise.
1 code implementation • SemEval (NAACL) 2022 • Amina Miftahova, Alexander Pugachev, Artem Skiba, Katya Artemova, Tatiana Batura, Pavel Braslavski, Vladimir Ivanov
The first approach follows the token classification schema, in which each token is assigned with a tag.
2 code implementations • 4 Apr 2024 • Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov, Alexander Gasnikov
We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs).
Ranked #1 on Image Classification on CUB-200-2011
2 code implementations • 23 Oct 2023 • Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov, Vladimir Ivanov, Alexey Kornaev, Ivan Titov
Concept Bottleneck Models (CBMs) assume that training examples (e. g., x-ray images) are annotated with high-level concepts (e. g., types of abnormalities), and perform classification by first predicting the concepts, followed by predicting the label relying on these concepts.
1 code implementation • 21 Oct 2022 • Natalia Loukachevitch, Suresh Manandhar, Elina Baral, Igor Rozhkov, Pavel Braslavski, Vladimir Ivanov, Tatiana Batura, Elena Tutubalina
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
no code implementations • 13 Jun 2022 • Vladimir Ivanov, Valery Solovyev
The method has been evaluated on a large test set for English.
1 code implementation • 23 May 2022 • Ekaterina Artemova, Maxim Zmeev, Natalia Loukachevitch, Igor Rozhkov, Tatiana Batura, Vladimir Ivanov, Elena Tutubalina
In the test set the frequency of all entity types is even.
1 code implementation • NeurIPS 2021 • Vladimir Ivanov, Konstantinos Michmizos
With a top accuracy of $97. 61\%$ on MNIST, $97. 51\%$ on N-MNIST, and $85. 84\%$ on Fashion-MNIST, NALSM achieved comparable performance to current fully-connected multi-layer spiking neural networks trained via backpropagation.
1 code implementation • RANLP 2021 • Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura, Pavel Braslavski, Ilia Denisov, Vladimir Ivanov, Suresh Manandhar, Alexander Pugachev, Elena Tutubalina
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
no code implementations • SEMEVAL 2020 • Dmitry Grigorev, Vladimir Ivanov
The paper presents the solution of team {''}Inno{''} to a SEMEVAL 2020 task 11 {''}Detection of propaganda techniques in news articles{''}.
no code implementations • 29 Oct 2020 • Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura, Vladimir Ivanov, Veronika Sarkisyan, Elena Tutubalina, Ivan Smurov
We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency.
no code implementations • 26 Aug 2020 • Dmitry Grigorev, Vladimir Ivanov
The paper presents the solution of team "Inno" to a SEMEVAL 2020 task 11 "Detection of propaganda techniques in news articles".
1 code implementation • 1 Jul 2020 • Ekaterina Artemova, Tatiana Batura, Anna Golenkovskaya, Vitaly Ivanin, Vladimir Ivanov, Veronika Sarkisyan, Ivan Smurov, Elena Tutubalina
In this paper we present a corpus of Russian strategic planning documents, RuREBus.
1 code implementation • 12 Jun 2020 • Joe Booth, Vladimir Ivanov
Over the course of the last several years there was a strong interest in application of modern optimal control techniques to the field of character animation.
no code implementations • 27 Nov 2019 • Chen Wang, Chengyuan Deng, Vladimir Ivanov
Variational Autoencoders (VAEs) are powerful in data representation inference, but it cannot learn relations between features with its vanilla form and common variations.
7 code implementations • 4 Feb 2019 • Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
no code implementations • 14 Nov 2016 • Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves.