Zobrazeno 1 - 10
of 765
pro vyhledávání: '"A. Lobacheva"'
It is generally accepted that starting neural networks training with large learning rates (LRs) improves generalization. Following a line of research devoted to understanding this effect, we conduct an empirical study in a controlled setting focusing
Externí odkaz:
http://arxiv.org/abs/2410.22113
Inspired by recent research that recommends starting neural networks training with large learning rates (LRs) to achieve the best generalization, we explore this hypothesis in detail. Our study clarifies the initial LR ranges that provide optimal res
Externí odkaz:
http://arxiv.org/abs/2311.11303
Transfer learning and ensembling are two popular techniques for improving the performance and robustness of neural networks. Due to the high cost of pre-training, ensembles of models fine-tuned from a single pre-trained checkpoint are often used in p
Externí odkaz:
http://arxiv.org/abs/2303.03374
Autor:
E. V. Kashtanova, A. S. Lobacheva
Publikováno v:
Вестник университета, Vol 0, Iss 3, Pp 176-185 (2024)
The article is devoted to the study of the genesis of understanding the causes of bias and discrimination of human capital from traditional forms to the era of digitalisation and artificial intelligence (hereinafter referred to as AI). In this articl
Externí odkaz:
https://doaj.org/article/62e1cf8cf3694c7694fff6135a12e14a
A fundamental property of deep learning normalization techniques, such as batch normalization, is making the pre-normalization parameters scale invariant. The intrinsic domain of such parameters is the unit sphere, and therefore their gradient optimi
Externí odkaz:
http://arxiv.org/abs/2209.03695
Autor:
Bobrov, Evgeny, Troshin, Sergey, Chirkova, Nadezhda, Lobacheva, Ekaterina, Panchenko, Sviatoslav, Vetrov, Dmitry, Kropotov, Dmitry
Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machine learning (ML) can be successfully applied. In this paper, we study
Externí odkaz:
http://arxiv.org/abs/2112.14423
Autor:
Philippova, O.S., Lobacheva, N.V., Dmitriev, A.Yu., Tsarevskaya, T.J., Strokovskaya, T.E., Lennik, S.G.
Publikováno v:
In Journal of Cultural Heritage May-June 2024 67:302-312
Memorization studies of deep neural networks (DNNs) help to understand what patterns and how do DNNs learn, and motivate improvements to DNN training approaches. In this work, we investigate the memorization properties of SimCLR, a widely used contra
Externí odkaz:
http://arxiv.org/abs/2107.10143
Training neural networks with batch normalization and weight decay has become a common practice in recent years. In this work, we show that their combined use may result in a surprising periodic behavior of optimization dynamics: the training process
Externí odkaz:
http://arxiv.org/abs/2106.15739
Autor:
Danil I. Peregud, Valeria Yu. Baronets, Anna S. Lobacheva, Alexandr S. Ivanov, Irina V. Garmash, Olga S. Arisheva, Zhanna D. Kobalava, Sergey V. Pirozhkov, Natalia N. Terebilina
Publikováno v:
Egyptian Liver Journal, Vol 13, Iss 1, Pp 1-8 (2023)
Abstract Background and aim Brain-derived neurotrophic factor (BDNF) functions not only in the brain but also in peripheral tissues such as the liver. Genetic factors determine the development of alcohol dependence and somatic consequences of chronic
Externí odkaz:
https://doaj.org/article/7ed0b0219bc44c08a24f6ca61bd4b004