Zobrazeno 1 - 10
of 329
pro vyhledávání: '"Romanov, Alexey A."'
Publikováno v:
In International Journal of Engineering Science 1 January 2025 206
Autor:
Gyrichidi, Ntmitrii1 (AUTHOR) girikhidi0@gmail.com, Romanov, Alexey M.1 (AUTHOR) romanov@mirea.ru, Trofimov, Oleg V.1 (AUTHOR), Eroshenko, Stanislav A.2 (AUTHOR) s.a.eroshenko@urfu.ru, Matrenin, Pavel V.2,3 (AUTHOR) matrenin.2012@corp.nstu.ru, Khalyasmaa, Alexandra I.2 (AUTHOR) a.i.khaliasmaa@urfu.ru
Publikováno v:
Sensors (14248220). Jun2024, Vol. 24 Issue 11, p3494. 25p.
Autor:
Gupte, Amit, Romanov, Alexey, Mantravadi, Sahitya, Banda, Dalitso, Liu, Jianjie, Khan, Raza, Meenal, Lakshmanan Ramu, Han, Benjamin, Srinivasan, Soundar
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect. Even commercial OCR systems can produce questionable output depending on
Externí odkaz:
http://arxiv.org/abs/2108.02899
BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the current work, we focus on the interpretation of self-attention, which is one
Externí odkaz:
http://arxiv.org/abs/1908.08593
Autor:
Romanov, Alexey, De-Arteaga, Maria, Wallach, Hanna, Chayes, Jennifer, Borgs, Christian, Chouldechova, Alexandra, Geyik, Sahin, Kenthapadi, Krishnaram, Rumshisky, Anna, Kalai, Adam Tauman
There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) p
Externí odkaz:
http://arxiv.org/abs/1904.05233
Autor:
De-Arteaga, Maria, Romanov, Alexey, Wallach, Hanna, Chayes, Jennifer, Borgs, Christian, Chouldechova, Alexandra, Geyik, Sahin, Kenthapadi, Krishnaram, Kalai, Adam Tauman
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representati
Externí odkaz:
http://arxiv.org/abs/1901.09451
Akademický článek
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In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, each of them responsible for a specific aspect of th
Externí odkaz:
http://arxiv.org/abs/1808.09042
Autor:
Romanov, Alexey, Shivade, Chaitanya
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training.
Externí odkaz:
http://arxiv.org/abs/1808.06752
Publikováno v:
In International Journal of Solids and Structures 1 November 2022 254-255