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作者： Batsyn, Mikhail Kalyagin, Valery Laboratory of Algorithms and Technologies for Network Analysis Department of Applied Mathematics and Informatics The National Research University Higher School of Economics 136 Rodionova Street Nizhny Novgorod Russian Federation 603093 Russia

ISBN: (纸本)9781461455738

The distribution of the sum of independent random variables plays an important role in many problems of applied mathematics. In this chapter we concentrate on the case when random variables have a continuous distribution with a discontinuity (or a probability mass) at a certain point r. Such a distribution arises naturally in actuarial mathematics when a responsibility or a retention limit is applied to every claim payment. An analytical expression for the distribution of the sum of i.i.d. random variables, which have a uniform distribution with a discontinuity, is reported. © Springer Science+Business Media New York 2013.

关键词： Random variables

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Marine mammal calls detection in acoustic signals via gradient boosting model 6

Marine mammal calls detection in acoustic signals via gradie...

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6th Underwater Acoustics Conference and Exhibition, UACE 2021

作者： Salin, Mikhail Ponomarenko, Alexander Center of Hydroacoustics Institute of Applied Physics RAS Nizhny Novgorod603950 Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Niznhy Novgorod Russia

This paper is devoted to methods of processing hydrophone records in order to identify specific signals, produced by marine mammals. The aim of processing is to detect a signal according to a certain pattern against the background of non-stationary noise. Development of a robust detection algorithm is of great interest because it could help to assess marine mammal occurrence and distribution at greater temporal and spatial scales by excluding laborious task of manually analyzing acoustic data. In addition, such algorithm may be implemented in automatic systems designed to warn vessel crew to avoid potential collision with whales. The data, studied in this paper, contained the calls of North Atlantic right whales, that were collected in the Gulf of St. Lawrence. Machine learning is exploited to solve a signal detection problem. The model was trained by a set of manually labelled signal fragments. Each fragment is labelled whether a whale call is present or not in the fragment. The proposed detection method is based on the two-level stacked gradient boosting models (XGBoost algorithm). The first-level model deals with spectral features, extracted from the short signal frames. The second model accumulates estimated output for a group of subsequent frames. © 2021 Acoustical Society of America.

关键词： Signal detection

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Facial expression and attributes recognition based on multi-task learning of lightweight neural networks

arXiv

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arXiv 2021年

作者： Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

In this paper, the multi-task learning of lightweight convolutional neural networks is studied for face identification and classification of facial attributes (age, gender, ethnicity) trained on cropped faces without margins. The necessity to fine-tune these networks to predict facial expressions is highlighted. Several models are presented based on MobileNet, EfficientNet and RexNet architectures. It was experimentally demonstrated that they lead to near state-of-the-art results in age, gender and race recognition on the UTKFace dataset and emotion classification on the AffectNet dataset. Moreover, it is shown that the usage of the trained models as feature extractors of facial regions in video frames leads to 4.5% higher accuracy than the previously known state-of-the-art single models for the AFEW and the VGAF datasets from the EmotiW challenges. The models and source code are publicly available at https://***/HSE-asavchenko/*** Codes 68T10 Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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HSE-NN Team at the 4th ABAW Competition: Multi-task Emotion Recognition and Learning from Synthetic Images

arXiv

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arXiv 2022年

作者： Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

In this paper, we present the results of the HSE-NN team in the 4th competition on Affective Behavior analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward neural networks in the multi-task learning challenge. We obtain performance measure 1.3 on the validation set, which is significantly greater when compared to either performance of baseline (0.3) or existing models that are trained only on the s-Aff-Wild2 database. In the learning from synthetic data challenge, the quality of the original synthetic training set is increased by using the super-resolution techniques, such as Real-ESRGAN. Next, the MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our average validation F1 score is 18% greater than the baseline convolutional neural network. As a result, our team took the first place in the learning from synthetic data challenge and the third place in the multi-task learning *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices

arXiv

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arXiv 2022年

作者： Savchenko, Andrey V. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. As a result, our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior analysis in-the-wild (ABAW) Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.15-0.2 higher performance measures for validation sets in uni-task Expression Classification, Valence-Arousal Estimation and Expression Classification. Due to simplicity, our approach may be considered as a new baseline for all four *** Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Recommending Restaurants based on Classification of Photos from the Gallery of Mobile Device

Recommending Restaurants based on Classification of Photos f...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Andrey V. Savchenko Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

ISBN: (纸本)9781665489898

The restaurant recommender systems are important for travelers, but may suffer from the “cold start” problem for new users. In this paper, it is assumed that photos of food in a gallery of mobile device of a user reflect his or her gastronomic preferences. The novel pipeline for modeling of the user's food preferences is introduced. At first, scene recognition neural network is used to extract photos of the restaurants from a gallery. It is proposed to train the multi-task convolutional network to predict the presence of food in an input image and the attributes of the restaurant. The recognized types of cuisine are summarized in a profile of gastronomic preferences of the user. The restaurants in a given city are recommended based on these preferences and additional attributes of a restaurant such as cumulative rating. Experimental study for the Yelp datasets is provided. Several neural network architectures are compared, among which EfficientNet models demonstrated the best performance.

关键词： Performance evaluation Protocols Neural networks Urban areas Pipelines Predictive models Prediction algorithms

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A New Sport Teams Logo Dataset for Detection Tasks 1

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International Conference on Computer Vision and Graphics, ICCVG 2020

作者： Kuznetsov, Andrey Savchenko, Andrey V. St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this research we introduce a new labelled SportLogo dataset, that contains images of two kinds of sports: hockey (NHL) and basketball (NBA). This dataset presents several challenges typical for logo detection tasks. A huge number of occlusions and logo view changes during playing games lead to an ambiguity of a straightforward detection approach use. Another issue is logo style changes due to seasonal kits updates. In this paper we propose a two stage approach, in which, firstly, an input image is processed by a specially trained scene recognition convolutional neural network. Second, conventional object detectors are applied only for sport scenes. Experimental study contains results of different combinations of backbone and detector convolutional neural networks. It was shown that MobileNet + YOLO v3 solution provides the best quality results on the designed dataset (mAP = 0.74, Recall = 0.87). © 2020, Springer Nature Switzerland AG.

关键词： Sports

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Detection and recognition of food in photo galleries for analysis of user preferences 17th

Detection and recognition of food in photo galleries for an...

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17th International Conference on Image analysis and Recognition, ICIAR 2020

作者： Miasnikov, Evgeniy Savchenko, Andrey St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9783030503468

Food analysis is one of the most important parts of user preference prediction engines for recommendation systems in the travel domain. In this paper, we describe and study the neural network method that allows you to recognize food in a gallery of photos taken with mobile devices. The described method consists of three main stages, including the classification of scenes, food detection, and subsequent classification. An essential feature of the developed method is the use of lightweight neural network models, which allows its usage on mobile devices. The development of the method was carried out using both known open data and a proprietary data set. © Springer Nature Switzerland AG 2020.

关键词： Convolutional neural networks

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Device-Specific Facial Descriptors: Winning a Lottery with a SuperNet 27

Device-Specific Facial Descriptors: Winning a Lottery with a...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Savchenko, Andrey Maslov, Dmitry Makarov, Ilya Sber AI Lab Moscow Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia Moscow Russia ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia

ISBN: (纸本)9781643685489

We address the challenge of devising neural network architectures to extract facial descriptors across diverse mobile and edge *** neural architecture search, we introduce a novel framework that selects optimal subnetworks from a SuperNet using an evolutionary *** a surrogate gradient boosting classifier to avoid direct accuracy estimation of subnetworks on validation sets, our approach swiftly delivers the most efficient and accurate models tailored to specific devices within *** versatility through an Android demo app, our framework excels in tasks like face recognition and emotion understanding across various devices, achieving real-time processing and superior accuracy compared to existing mobile models. © 2024 The Authors.

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Discrete KP equation, generalized Volterra lattice and multiple orthogonal polynomials

Discrete KP equation, generalized Volterra lattice and multi...

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International Conference on Orthogonal Polynomials,Integrable Systems and Their Applications on the Occasion of Prof. Mourad Ismails 70th Birthday(2014正交多项式国际会议)

作者： Valeriy A. Kalyagin National Research University Higher School of Economics Laboratory of Algorithms and technologies for Network AnalysisRussian

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