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On Power Laws in Deep Ensembles
P. 2375–2385.
In book
Curran Associates, Inc., 2020.
Prokhorov A., Wei Z., Sang H. et al., Journal of Productivity Analysis 2026 Vol. 65 P. 1–16
The stochastic frontier model (SFM) is widely employed in the analysis of productivity and efficiency, yet strict parametric forms, such as the Cobb-Douglas and Translog functions, are often assumed for modeling production, leading to potential misspecification issues. While semi- and nonparametric SFMs offer greater flexibility, they face challenges in imposing monotonicity and concavity to maintain ...
Added: September 28, 2026
Pikul A. S., Безопасность информационных технологий 2024 Т. 31 № 4 С. 116–127
This article explores the potential use of modern computer vision architectures for the task of deepfake detection. The following architectures are considered: EfficientNet, Vision Transformer (ViT), VisionLSTM (ViL), Vision KAN, and Mamba Vision. The novelty of the approach lies in the application and comparison of these architectures, as well as their combination into paired ensembles ...
Added: December 12, 2025
V.P. Stepashkina, M.I. Hushchyn, Doklady Mathematics 2024 Vol. 110 No. 1 P. S95–S102
This paper presents the development and evaluation of methods for detecting cyberattacks on industrial systems using neural network approaches. The focus is on the task of detecting anomalies in multivariate time series, where the diversity and complexity of potential attack scenarios require the use of advanced models. To address these challenges, a transformer-based autoencoder architecture ...
Added: March 25, 2025
Kulyasova E. V., Kulyasov N.S., Puchkov A. Y., , in: Journal of Physics: Conference Series Volume 1260, 2019 Mechanical Science and Technology Update 23–24 April 2019, Omsk, Russian Federation.: IOP Publishing, 2019. Ch. 3 P. 032024–032024.
This article is introduced into the perspective tendencies of the digital transformation of chemical enterprises which allow to improve the process of managing enterprises of the branch. Presented the algorithms of managing and technological information processing based on deep neural network apparatus. New approaches to data processing known as video analytics are applied; it allows ...
Added: September 27, 2024
Sadrtdinov I., Dmitrii Pozdeev, Dmitry P Vetrov et al., , in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023).: Curran Associates, Inc., 2023. P. 15936–15964.
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 practice. Such models end up in the same basin of the loss landscape, which we call the pre-train ...
Added: February 26, 2024
Чупров И. А., Гао Ц., Efremenko D. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2023 Т. 514 № 2 С. 28–38
Физико-информированные нейронные сети (Physics Informed Neural Networks – PINN) являются перспективным методом решения уравнений в частных производных с помощью машинного обучения. В работе рассмотрено применение PINN к нелинейному уравнению Шредингера для описания ...
Added: December 19, 2023
Захарова Т. В., Yuzhakov T., ООО «Макс Пресс», 2019.
В настоящий сборник вошли тезисы докладов секции Вычислительной математики и кибернетики конференции «Ломоносовские чтения‑2019», проводимой Московским государственным университетом имени М. В. Ломоносова в 2019 году. ...
Added: December 13, 2023
Nakhodnov M., Kodryan M., Lobacheva E. et al., , in: Doklady MathematicsVol. 106. Issue 1: Supplement.: Pleiades Publishing, Ltd. (Плеадес Паблишинг, Лтд), 2023. P. 43–62.
Knowledge of the loss landscape geometry makes it possible to successfully explain the behavior of neural networks, the dynamics of their training, and the relationship between resulting solutions and hyperparameters, such as the regularization method, neural network architecture, or learning rate schedule. In this paper, the dynamics of learning and the surface of the standard ...
Added: June 9, 2023
Kodryan M., Lobacheva E., Nakhodnov M. et al., , in: Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022.: Curran Associates, Inc., 2022. P. 14058–14070.
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 optimization dynamics can be represented via spherical optimization with varying effective learning rate (ELR), which was studied previously. However, the varying ELR ...
Added: December 20, 2022
Belomestny D., Naumov A., Puchkin N. et al., Neural Networks 2023 Vol. 161 P. 242–253
This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a deep neural network to approximate any Hölder smooth function up to a given approximation error in Hölder norms in such a way that all weights of this neural network are bounded ...
Added: July 13, 2022
Lobacheva E., Kodryan M., Chirkova N. et al., , in: Advances in Neural Information Processing Systems 34 (NeurIPS 2021).: Curran Associates, Inc., 2021. P. 21545–21556.
Added: December 29, 2021
Malinin A., Mlodozeniec B., Gales M., , in: Proceedings of the 8th International Conference on Learning Representations (ICLR 2020).: ICLR, 2020.
Added: November 1, 2021
Sokolov A., Savchenko A., , in: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI).: IEEE, 2021. P. 413–418.
This paper is focused on the finetuning of acoustic models for speaker adaptation goals on a given gender. We pretrained the Transformer baseline model on Librispeech-960 and conducted experiments with finetuning on the gender-specific test subsets. The obtained word error rate (WER) relatively to the baseline is up to 5% and 3% lower on male ...
Added: September 26, 2021
Belavin V., Ustyuzhanin A., Sergey Shirobokov et al., , in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020).: Curran Associates, Inc., 2020. P. 14650–14662.
Added: February 14, 2021
Beknazarov N., Jin S., Poptsova M., Scientific Reports 2020 Vol. 10 P. 19134
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not ...
Added: December 11, 2020
Sokolov A., / Series Computer Science "arxiv.org". 2021.
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challenging to explain the effect of each information stream on the SER systems. Further, more clarification is required for analysing the impact of ASR's word error rate (WER) on linguistic emotion ...
Added: November 17, 2020
Lobacheva E., Chirkova N., Markovich A. et al., , in: Thirty-Fourth AAAI Conference on Artificial IntelligenceVol. 34.: AAAI Press, 2020. Ch. 5938 P. 4989–4996.
Added: October 29, 2020
Demochkina P., Savchenko A., , in: Proceedings of IEEE International Russian Automation Conference (RusAutoCon 2020).: IEEE, 2020. Ch. 110 P. 610–614.
In this paper, we address the problem of detecting small objects on high-quality X-ray imagesusing deep neural networks. We propose to implement the two-stage approach, in which, firstly, input image issplit into partially overlapping blocks to make small objects more discriminative for detection. Secondly, the small blocks are fed into conventional single-shot detectors. These detectors ...
Added: October 3, 2020
Savchenko A., IEEE Transactions on Neural Networks and Learning Systems 2020 Vol. 31 No. 2 P. 651–660
If the training data set in image recognition task is not very large, the feature extraction with a convolutional neural network is usually applied. Here, we focus on the nonparametric classification of extracted feature vectors using the probabilistic neural network (PNN). The latter is characterized by the high runtime and memory space complexity. We propose ...
Added: November 1, 2019