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Expressive power of recurrent neural networks
P. 1–12.
Khrulkov V., Novikov A., Oseledets I.
Language:
English
In book
[б.и.], 2018.
Kodryan M., Kropotov D., Vetrov D., , in: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Volume 206Vol. 206.: Valencia: PMLR, 2023. P. 3718–3732.
Tensor decomposition methods have proven effective in various applications, including compression and acceleration of neural networks. At the same time, the problem of determining optimal decomposition ranks, which present the crucial parameter controlling the compressionaccuracy trade-off, is still acute. In this paper, we introduce MARS - a new efficient method for the automatic selection of ...
Added: June 9, 2023
Kodryan M., Kropotov D., Vetrov D., / Series QTNML 2020 "First Workshop on Quantum Tensor Networks in Machine Learning, NeurIPS 2020". 2020.
Tensor decomposition methods have recently proven to be efficient for compressing and accelerating neural networks. However, the problem of optimal decomposition structure determination is still not well studied while being quite important. Specifically, decomposition ranks present the crucial parameter controlling the compression-accuracy trade-off. In this paper, we introduce MARS - a new efficient method for ...
Added: February 5, 2021
Shitov Y., Linear Algebra and its Applications 2018 Vol. 544 P. 299–305
An n×n matrix A is called permutative if the rows of A are distinct permutations of a family of n distinct elements. For all n⩾3, we show that the minimal rank of a non-negative permutative matrix equals 3. The minimal rank of a generic permutative n×n matrix equals the smallest integer r such that r!⩾n. ...
Added: January 30, 2019
Izmailov P., Novikov A., Kropotov D., , in: Proceedings of Machine Learning Research. Proceedings of The International Conference on Artificial Intelligence and Statistics (AISTATS 2018).: [б.и.], 2018. P. 726–735.
We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs ...
Added: December 10, 2018
Voronkov Ilia, Amajd M., Kaimuldenov Z., , in: Actual Problems of System and Software Engineering 2017. Proceedings of the 5th International Conference on Actual Problems of System and Software Engineering Supported by Russian Foundation for Basic Research. Project #17-07-20565 Moscow, Russia, November 14-16, 2017, 408 P.Vol. 1989.: Aachen: CEUR Workshop Proceedings, 2017. P. 362–370.
In this paper, we analyze the use of different neural networks for the
text classification task. The accuracy of the studied text classifiers can be
changed by a small number of previously classified texts. This is important due
to the fact that in many applications of text classification a large number of unlabeled texts are easily accessible, while ...
Added: August 16, 2018
Izmailov P., Novikov A., Kroptov D., / Series arXiv "math". 2017.
We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs ...
Added: October 20, 2017
A. G. Rassadin, A. V. Savchenko, , in: CEUR Workshop ProceedingsVol. 1901: Proceedings of the International conference Information Technology and Nanotechnology. Session Image Processing, Geoinformation Technology and Information Security.: CEUR-WS, 2017. P. 207–213.
In this paper, we consider the problem of insufficient runtime and memory space complexities of deep convolutional neural networks for visual emotion recognition. A survey of recent compression methods and efficient neural networks architectures is provided. We experimentally compare the computational speed and memory consumption during the training and the inference stages of such methods ...
Added: October 17, 2017
Пономарева М. А., Milintsevich K., Artemova E. et al., , in: Proceedings of the First Workshop on Subword and Character Level Models in NLP.: Stroudsburg, PA: Association for Computational Linguistics, 2017. P. 31–35.
Abstract In this study we address the problem of automated word stress detection in Russian using character level models and no partspeech-taggers. We use a simple bidirectional RNN with LSTM nodes and achieve the accuracy of 90% or higher. We experiment with two training datasets and show that using the data from an annotated corpus is ...
Added: October 10, 2017
Novikov A., Trofimov M., Oseledets I., / Series stat :: arxiv :: Cornell University "stat :: arxiv :: Cornell University". 2017.
Modeling interactions between features improves the performance of machine learning solutions in many domains (e.g. recommender systems or sentiment analysis). In this paper, we introduce Exponential Machines (ExM), a predictor that models all interactions of every order. The key idea is to represent an exponentially large tensor of parameters in a factorized format called Tensor ...
Added: September 19, 2016
Vetrov D., Osokin A., Rodomanov A. et al., Journal of Machine Learning Research 2014
In the paper we present a new framework for dealing with probabilistic graphical models. Our approach relies on the recently proposed Tensor Train format (TT-format) of a tensor that while being compact allows for efficient application of linear algebra operations. We present a way to convert the energy of a Markov random field to the ...
Added: March 18, 2015
Vetrov D., Osokin A., Novikov A. et al., , in: JMLR Workshop and Conference ProceedingsIssue 32: Proceedings of The 31st International Conference on Machine Learning.: Beijing: Microtome Publishing, 2014. P. 811–819.
In the paper we present a new framework for dealing with probabilistic graphical models. Our approach relies on the recently proposed Tensor Train format (TT-format) of a tensor that while being compact allows for efficient application of linear algebra operations. We present a way to convert the energy of a Markov random field to the ...
Added: March 14, 2015
Lenko G. N., Вестник Ленинградского государственного университета им. А.С. Пушкина 2015 Т. 1 № 1 С. 84–91
В данной статье анализируется категория эмотивности и рассматривается соотношение данной категории с такими смежными понятиями, как эмоциональность, экспрессивность, оценочность (оценка) и образность. ...
Added: October 24, 2014