Proceedings of the Workshop of the 5th International Conference on Learning Representations (ICLR)
Deep neural networks and maximum likelihood search for approximate nearest neighbor in video-based image recognition
, Optical Memory and Neural Networks (Information Optics) 2017 Vol. 26 No. 2 P. 129-136
We analyzed the way to increase computational efficiency of video-based image recognition methods with matching of high dimensional feature vectors extracted by deep convolutional neural networks. We proposed an algorithm for approximate nearest neighbor search. At the first step, for a given video frame the algorithm verifies a reference image obtained when recognizing the previous ...
Added: June 30, 2017
, , mPyPl: Python Monadic Pipeline Library for Complex Functional Data Processing / Cornell University. Series Computer Science "arxiv.org". 2021.
In this paper, we present a new Python library called mPyPl, which is intended to simplify complex data processing tasks using functional approach. This library defines operations on lazy data streams of named dictionaries represented as generators (so-called multi-field datastreams), and allows enriching those data streams with more 'fields' in the process of data preparation ...
Added: October 7, 2021
, , , Bayesian Group Sparsification of Long Short-Term Memory Networks / . 2018.
We propose a new Bayesian sparsification technique for gated recurrent architectures that encounters for its recurrent specifics and gated mechanism. Our method eliminates neurons from the model and makes gates constant, not only compressing the network, but also significantly accelerating a forward pass. On the discriminative tasks our method compresses LSTM extremely, so that only ...
Added: October 16, 2018
Added: September 2, 2019
, , , Bulletin of the Polish Academy of Sciences: Technical Sciences 2018 Vol. 66 No. 6 P. 811-820
We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables expresses the preference for faster computation. The amount of computation for an input is determined via amortized maximum a posteriori (MAP) inference. MAP inference is performed ...
Added: February 27, 2019
, , , Optical Memory and Neural Networks (Information Optics) 2018 Vol. 27 No. 1 P. 23-31
We discuss the video classification problem with the matching of feature vectors extracted using deep convolutional neural networks from each frame. We propose the novel recognition method based on representation of each frame as a sequence of fuzzy sets of reference classes whose degrees of membership are defined based on asymptotic distribution of the Kullback–Leibler ...
Added: February 9, 2018
, , et al., Proceedings of Machine Learning Research 2020 P. 1-9
We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the simulator is stochastic. To address such cases, we introduce the use ...
Added: October 31, 2019
, Real-time Streaming Wave-U-Net with Temporal Convolutions for Multichannel Speech Enhancement / Cornell University. Series Computer Science "arxiv.org". 2021.
In this paper we describe our work that we have done to participate in Task1 of ConferencingSpeech2021 challenge. This task set a goal to develop the solution for multi-channel speech enhancement in a real-time manner. We propose a novel system for streaming speech enhancement. We employ Wave-U-Net architecture with temporal convolutions in encoder and decoder. ...
Added: September 26, 2021
Система постановки произношения на основе сверточных нейронных сетей и информационной теории восприятия речи
, Информационные технологии 2019 Т. 25 № 5 С. 313-318
We consider a problem of computer assisted language and pronunciation learning based on the deep learning methods and the information theory of speech perception. In order to improve the efficiency of testing of pronunciation quality, we propose to train a convolutional neural network using the best reference utterances from the user. The experimental results proved ...
Added: May 29, 2019
Deep convolutional neural networks capabilities for binary classification of polar mesocyclones in satellite mosaics
, , et al., Atmosphere 2018 Vol. 9 No. 426 P. 1-23
Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed ...
Added: November 26, 2020
Self-driving cars and advanced safety features present one of today’s greatest challenges and opportunities for Artificial Intelligence (AI). Despite billions of dollars of investments and encouraging progress under certain operational constraints, there are no driverless cars on public roads today without human safety drivers. Autonomous Driving research spans a wide spectrum, from modular architectures -- ...
Added: December 28, 2020
Proceeding of sixth International Conference on Learning Representations (ICLR 2018) ...
Added: February 4, 2018
Workshop on Compact Deep Neural Network Representation with Industrial Applications, Thirty-second Conference on Neural Information Processing Systems
Montréal: [б.и.], 2018
This workshop aims to bring together researchers, educators, practitioners who are interested in techniques as well as applications of making compact and efficient neural network representations. One main theme of the workshop discussion is to build up consensus in this rapidly developed field, and in particular, to establish close connection between researchers in Machine Learning ...
Added: December 5, 2018
Intelligent Data Processing 11th International Conference, IDP 2016, Barcelona, Spain, October 10–14, 2016, Revised Selected Papers
Switzerland: Springer, 2019
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016. The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life ...
Added: February 8, 2020
, , , Bayesian Sparsification of Recurrent Neural Networks / . 2017.
Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights. Recently proposed Sparse Variational Dropout (Molchanov et al., 2017) eliminates the majority of the weights in a feed-forward neural network without significant loss of quality. We apply this technique to sparsify recurrent neural ...
Added: October 19, 2017
Интерфейс мозг-компьютер: опыт построения, использования и возможные пути повышения рабочих характеристик
, , et al., Журнал высшей нервной деятельности им. И.П. Павлова 2017 Т. 67 № 4 С. 504-520
Brain-computer interfaces find application in a number of different areas and have the potential to be used for research as well as for practical purposes. The clinical use of BCI includes current studies on neurorehabilitation ([Frolov et al., 2013; Ang et al., 2010]), and there is the prospect of using BCI to restore movement and ...
Added: October 19, 2017
, , et al., Frontiers in Pharmacology 2020 Vol. 11 P. 1-10
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is ...
Added: April 21, 2021
, , , Journal of Physics: Conference Series 2021 Vol. 1740 Article 012031
Demographic and population structure inference is one of the most important problems in genomics. Population parameters such as effective population sizes, population split times and migration rates are of high interest both themselves and for many applications, e.g. for genome-wide association studies. Hidden Markov Model (HMM) based methods, such as PSMC, MSMC, coalHMM etc., proved ...
Added: May 17, 2021
Piscataway: IEEE, 2020
2020 International Joint Conference on Neural Networks (IJCNN) held virtually, as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) 2020. IJCNN 2020 is jointly organized by the IEEE Computational Intelligence Society (CIS) and the International Neural Network Society (INNS). For IJCNN 2020 (and when WCCI is organized in even-numbered years) IEEE CIS ...
Added: October 15, 2020
, , Microsoft Journal of Applied Research, USA 2019 Vol. 12 P. 140-150
In this paper, we present a new Python library called mPyPl, which is intended to simplify complex data processing tasks using a functional approach. This library defines operations on lazy data streams of named dictionaries represented as generators (so-called multi-field datastreams), and allows enriching those data streams with more ’fields’ in the process of data ...
Added: November 20, 2020
Распознавание изолированных слов на основе взвешенного голосования дикторозависимых нейросетевых моделей
, Информационные технологии 2020 Т. 26 № 5 С. 290-296
article deals with the problem of isolated words recognition based on deep convolutional neural networks. The use of existing recognition systems in practice is limited by an insufficiently high degree of their reliability functioning in conditions of intense acoustic noise, such as street noise, sounds from passing vehicles, etc. Nowadays, the most accurate recognition methods are characterized by ...
Added: September 2, 2020
Cham: Springer, 2019
Intelligent Systems Conference (IntelliSys) 2018 is the fourth research conference in the series. This conference is a part of SAI conferences being held since 2013. The conference series has featured keynote talks, special sessions, poster presentation, tutorials, workshops, and contributed papers each year. The conference focus on areas of intelligent systems and artificial intelligence (AI) and ...
Added: August 29, 2018
, , , 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
, , et al., Journal of Machine Learning Research 2016 Vol. 51 P. 130-138
The recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words. However, Skip-gram as well as most prior work on learning word representations does not take into account word ambiguity and maintain only single representation per word. Although a number of Skip-gram modifications were proposed ...
Added: October 1, 2016