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14th International Conference on Optimization and Applications, OPTIMA 2023

作者： Kalyagin, Valeriy Kostylev, Ilya Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

ISBN: (纸本)9783031487507

Graphical models became a popular tool in machine learning and data analysis. Graphical Model Selection Problem is a problem to recover a specific graph structure (graphical model) from a set of data. In many cases the data are given by a sample of observations of some multivariate distribution. In this setting any reconstruction algorithm can be evaluated by uncertainty of identification of the hidden graphical model by observations. In the present paper we study uncertainty of identification of so-called concentration graph which represents a dependence structure for the components of multidimensional random vector. We introduce and discuss different measures of uncertainty appropriate for the concentration graph identification problem and compare on this basis different identification algorithms, including optimization (graphical lasso) algorithm and a family of known multiple hypotheses testing algorithms. Novelty of our approach is in the study of dependence of uncertainty on the graph density. Some new and interesting phenomena are observed and discussed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Concentration graph Graph density Graphical model Optimal identification algorithm Uncertainty

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Uncertainty of Graph Clustering in Correlation Block Model 22nd

Uncertainty of Graph Clustering in Correlation Block Model

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22nd International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2023

作者： Aroslankin, Artem Kalyagin, Valeriy Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia

ISBN: (纸本)9783031432569

Cluster analysis is a powerful tool in network science and it is well developed in many directions. However, the uncertainty analysis of clustering algorithms is still not sufficiently investigated in the literature. To study uncertainty of clustering algorithms we propose to use a new model, which we call correlation block model. We suggest to measure uncertainty of clustering algorithms by error in cluster identification by observations. Uncertainty of different clustering algorithms are compared using proposed methodology. New and interesting phenomena are observed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Temporal Modeling via TCN and Transformer for Audio-Visual Emotion Recognition 12th

Temporal Modeling via TCN and Transformer for Audio-Visua...

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12th International Conference on analysis of Images, Social networks and Texts, AIST 2024

作者： Andreev, Aleksei A. Savchenko, Andrey V. Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia Sber AI Lab Moscow Russia

ISBN: (纸本)9783031880353

Emotion recognition from multimodal sources is essential for advancing human-computer interaction. This paper introduces a comprehensive approach integrating robust methodologies to enhance multimodal emotion recognition. By harnessing both facial video and audio signals through Temporal Convolutional networks (TCN) and Transformer models, alongside other advanced neural architectures, our ensemble effectively captures the complex dynamics of emotional expressions. Our approach has been rigorously evaluated on the Expression Classification challenge from the recent Affective Behavior analysis in-the-Wild competition, demonstrating significant improvements compared to state-of-the-art methods. Specifically, our model achieved a 12% increase in F1 Score over the baseline, illustrating a substantial enhancement in performance and reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Finding Weakly Correlated Nodes in Random Variable networks

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Operations Research Forum 2024年第4期5卷 1-14页

作者： Koldanov, Petr Koldanov, Alexander Semenov, Dmitry Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics 136 Rodionova street Nizhny Novgorod 603093 Russian Federation

The issue of identifying sets of weakly correlated stocks is explored. Four distinct methods for constructing these sets are compared: the traditional approach using Pearson correlation, the traditional approach using Kendall correlation, and multiple hypothesis testing methods, which apply both Pearson and Kendall correlations. To derive specific findings, we analyze daily returns of a selection of stocks listed on the Frankfurt (FWB), London (LSE), and Paris (Euronext Paris) stock exchanges. Our results reveal a significant difference between the identified sets of weakly correlated stocks in Pearson and Kendall correlation networks. Notably, this difference is more substantial in the statistically significant sets of weakly correlated stocks derived from multiple hypothesis testing methods than in those obtained through traditional procedures. We recommend for the use of multiple hypothesis testing methods based on Kendall correlation for analyzing market data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Family-wise error rate (FWER) Kendall correlation Multiple hypotheses testing procedure Pearson correlation Random variable network Statistical significance Stock returns Weakly correlated stocks

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Robustness of Graphical Lasso Optimization Algorithm for Learning a Graphical Model 23rd

Robustness of Graphical Lasso Optimization Algorithm for L...

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23rd International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2024

作者： Kalyagin, Valeriy Kostylev, Ilya HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia Steklov Mathematical Institute Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031627910

Problem of learning a graphical model (graphical model selection problem) consists of recovering a conditional dependence structure (concentration graph) from data given as a sample of observations from a random vector. Various algorithms to solve this problem are known. One class of algorithms is related with convex optimization problem with additional lasso regularization term. Such algorithms are called graphical lasso algorithms. Various properties and practical efficiency of graphical lasso algorithms were investigated in the literature. In the present paper we study sensitivity of uncertainty (level of error) of graphical lasso algorithms to the change of distribution of the random vector. This issue is not well studied yet. First, we show that uncertainty of the classical version of graphical lasso algorithm is very sensitive to the change of distribution. Next, we suggest simple modifications of this algorithm which are much more robust in the large class of distributions. Finally, we discuss a future development of the proposed approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Graphic methods

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EmotiEffNets for Facial Processing in Video-based Valence-Arousal Prediction, Expression Classification and Action Unit Detection

EmotiEffNets for Facial Processing in Video-based Valence-Ar...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Savchenko, Andrey V. Sber Ai Lab Moscow Russia Hse University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9798350302493

In this article, the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction are used for downstream emotion analysis tasks from the fifth Affective Behavior analysis in-the-wild (ABAW) competition. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model is much better than the baseline facial processing using VGGFace And ResNet. For example, our macro-averaged F1-scores of facial expression recognition and action unit detection on the testing set are 11-13% greater. Moreover, the concordance correlation coefficients for valence/arousal estimation are up to 30% higher when compared to the baseline. © 2023 IEEE.

关键词： Emotion Recognition

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MT-EmotiEffNet for Multi-task Human Affective Behavior analysis and Learning from Synthetic Data 17th

MT-EmotiEffNet for Multi-task Human Affective Behavior Anal...

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17th European Conference on Computer Vision, ECCV 2022

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

ISBN: (纸本)9783031250743

In this paper, we present the novel multi-task EfficientNet model and its usage in the 4th competition on Affective Behavior analysis in-the-wild (ABAW). This 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 model. As a result, our team took the first place in the learning from synthetic data task and the third place in the multi-task learning challenge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blending

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Recommending Restaurants based on Classification of Photos from the Gallery of Mobile Device 20

Recommending Restaurants based on Classification of Photos f...

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20th IEEE Jubilee International Symposium on Intelligent Systems and Informatics, SISY 2022

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

ISBN: (纸本)9781665489881

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. © 2022 IEEE.

关键词： Recommender systems

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Efficient algorithms for Video-Based Engagement Prediction for a MOOC Course

Efficient Algorithms for Video-Based Engagement Prediction f...

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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Demochkina, Polina Savchenko, Andrey Hse University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

In this paper, we address the engagement prediction task using a two-stage approach. First, two types of features are extracted from each frame on the input video using the OpenFace toolkit and an EfficientNet-based model that was pre-trained for the task of age, gender, and identity prediction. Each set of obtained feature vectors is then separately aggregated into a single video descriptor using statistical functions and a prediction is made using a linear support vector machine. Finally, the scores are fused, and the engagement intensity is predicted. The described ensemble model achieves MSE of 0.0534 on the test set of the EngageWild dataset from the EmotiW 2020 challenge. © 2022 IEEE.

关键词： Computer vision

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Multi-user facial emotion recognition in video based on user-dependent neural network adaptation 8

Multi-user facial emotion recognition in video based on user...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

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

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

In this paper, the multi-user video-based facial emotion recognition is examined in the presence of a small data set with the emotions of end users. By using the idea of speaker-dependent speech recognition, we propose a novel approach to solve this task if labeled video data from end users is available. During the training stage, a deep convolutional neural network is trained for user-independent emotion classification. Next, this classifier is adapted (fine-tuned) on the emotional video of a concrete person. During the recognition stage, the user is identified based on face recognition techniques, and an emotional model of the recognized user is applied. It is experimentally shown that this approach improves the accuracy of emotion recognition by more than 20% for the RAVDESS dataset. © 2022 IEEE.

关键词： Emotion Recognition

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