• A
  • A
  • A
  • АБВ
  • АБВ
  • АБВ
  • A
  • A
  • A
  • A
  • A
Обычная версия сайта
  • RU
  • EN
  • HSE University
  • Publications
  • Books
  • Proceedings of 18th International Conference on Machine Learning and Computing
  • RU
  • EN
Расширенный поиск
Высшая школа экономики
Национальный исследовательский университет
Priority areas
  • business informatics
  • economics
  • engineering science
  • humanitarian
  • IT and mathematics
  • law
  • management
  • mathematics
  • sociology
  • state and public administration
by year
  • 2028
  • 2027
  • 2026
  • 2025
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2010
  • 2009
  • 2008
  • 2007
  • 2006
  • 2005
  • 2004
  • 2003
  • 2002
  • 2001
  • 2000
  • 1999
  • 1998
  • 1997
  • 1996
  • 1995
  • 1994
  • 1993
  • 1992
  • 1991
  • 1990
  • 1989
  • 1988
  • 1987
  • 1986
  • 1985
  • 1984
  • 1983
  • 1982
  • 1981
  • 1980
  • 1979
  • 1978
  • 1977
  • 1976
  • 1975
  • 1974
  • 1973
  • 1972
  • 1971
  • 1970
  • 1969
  • 1968
  • 1967
  • 1966
  • 1965
  • 1964
  • 1963
  • 1958
  • More
Subject
News
September 18, 2026
When Pictures Hinder Understanding: Illustrations May Impede Learning of Abstract Ideas
Illustrations can help remember specific actions but do not always make abstract ideas easier to learn. Researchers from HSE University and Humboldt University compared how people learn from texts with different levels of abstractness. They found that participants remembered illustrations better and performed better on related tasks after reading a multimedia text about yoga asanas than after reading an abstract text about the Nash equilibrium. The findings could help improve the selection of illustrations for educational and informational materials. The study has been published in Learning and Instruction.
September 17, 2026
'I Wish That People Would Place Greater Trust in Science'
When Tatiana Eremicheva chose Fundamental and Computational Linguistics as her field of study, she thought it would be about learning languages. Instead, she discovered it was about helping people. In this interview for the HSE Young Scientists project, she discusses science as a way of understanding the world, billiards as a team-building activity, and why learning to read is not always as easy as it seems.
September 15, 2026
Immunity to Chaos: How Personal Resources Help Us Cope with the Challenges of a Turbulent World
International conflicts, crises and digital overload—the modern world puts our minds to the test every day. Traditional psychology often focuses on the consequences: anxiety, depression, and psychosomatic disorders. But what if we looked at the problem differently—through the lens of the resources that prevent us from breaking down? Psychological immunity is precisely this set of resources. Alena Zolotareva and her group, Psychological Immunity as a Resource for Positive Functioning, are developing an integrative model of this phenomenon, adapting diagnostic tools and preparing for large-scale empirical research. Why do psychologists need to collaborate with medical professionals, and how could their research transform preventive care in clinics and corporations?

 

Have you spotted a typo?
Highlight it, click Ctrl+Enter and send us a message. Thank you for your help!

Publications
  • Books
  • Articles
  • Chapters of books
  • Working papers
  • Report a publication
  • Research at HSE

?

Proceedings of 18th International Conference on Machine Learning and Computing

Vol. 2061. Springer, Cham, 2026.

-

Chapters
Hybrid Non-local Blocks for Streptococci Segmentation in Microscopic Images
Aleksei Samarin, Nazarenko A., Kotenko E. et al., , in: Proceedings of 18th International Conference on Machine Learning and ComputingVol. 2061.: Springer, Cham, 2026. Ch. 26 P. 373–385.
Modern knowledge and large volumes of data are increasingly encoded within neural networks, making the task of simplifying their structures and reducing the number of parameters especially relevant, both to improve efficiency and to facilitate deployment in resource-constrained environments. This paper presents a novel approach to neural network compression that addresses redundancy at both the ...
Added: September 19, 2026
Research target: Computer Science Mathematics
Language: English
Text on another site
Keywords: deep learning
Proceedings of 18th International Conference on Machine Learning and Computing
Similar publications
Advances in Neural Computation, Machine Learning, and Cognitive Research IX
Springer, Cham, 2026.
computer vision ...
Added: September 19, 2026
IDAP++: Advancing Divergence-Aware Pruning with Joint Filter and Layer Optimization
Aleksei Samarin, Nazarenko A., Kotenko E. et al., Proceedings of the ACM on Management of Data 2026 Vol. 4 No. 1 P. 1–28
Modern knowledge and large volumes of data are increasingly encoded within neural networks, making the task of simplifying their structures and reducing the number of parameters especially relevant, both to improve efficiency and to facilitate deployment in resource-constrained environments. This paper presents a novel approach to neural network compression that addresses redundancy at both the ...
Added: September 19, 2026
Proceedings of the 35th Conference of Open Innovations Association FRUCT
FRUCT Oy, 2024.
Added: September 19, 2026
AutoML Applications for Bacilli Recognition by Taxonomic Characteristics Determination over Microscopic Images
Aleksei Samarin, Aleksei Toropov, Dzestelova A. et al., , in: Proceedings of the 36th Conference of Open Innovations Association FRUCTVol. 36.: FRUCT Oy, 2024. P. 903–911.
In this work, we describe our research aimed at developing classifiers for microbial images (bacilli images) obtained through microscopy of live (non-static) samples. We employed our proposed approach called AutoML, which is based on the automatic generation and analysis of the feature space to create the most optimal descriptors for microscopic images used in their ...
Added: September 19, 2026
Proceedings of the 36th Conference of Open Innovations Association FRUCT
FRUCT Oy, 2024.
Added: September 19, 2026
Proceedings of the 37th Conference of Open Innovations Association FRUCT
FRUCT Oy, 2025.
Added: September 19, 2026
Efficient Pruning Optimization for Trainable Descriptor-Based Facade Signboard Classification
Aleksei Samarin, Aleksei Toropov, Nazarenko A. et al., , in: Proceedings of the 39th Conference of Open Innovations Association FRUCTVol. 39.: FRUCT Oy, 2026. P. 253–261.
Commercial facade service classification from streetlevel imagery benefits from multi-branch pipelines that fuse global facade appearance with signboard-centric cues; however, this design increases inference cost and hinders large-scale deployment. We study pruning for a multi-branch facade recognition pipeline with a trainable signboard descriptor and compare unstructured magnitude pruning with structured baselines, including channel pruning and ...
Added: September 19, 2026
Proceedings of the 39th Conference of Open Innovations Association FRUCT
FRUCT Oy, 2026.
Added: September 19, 2026
Refined Non-Local Blocks for Precise Segmentation of Diplococci in Microscopy Imagery
Aleksei Samarin, Kotenko E., Aleksei Toropov et al., , in: ICICT 2026: Proceedings of the 2026 9th International Conference on Information and Computer Technologies.: Association for Computing Machinery (ACM), 2026. P. 155–161.
This study investigates the integration of attention mechanisms within deep learning architectures, specifically focusing on the segmentation of diplococci microorganisms in microscopy images. We introduce novel modifications to attention blocks, tailored to address challenges inherent to microscopy imaging, such as blurred boundaries and indistinct microorganism features. Our experimental results demonstrate significant performance improvements compared to ...
Added: September 19, 2026
Flow-Guided Neural Pruning: Signal-Flow Framework for Multi-Architecture Model Compression
Aleksei Samarin, Nazarenko A., Kotenko E. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 8 P. 1–26
This paper presents a novel method for pruning deep neural networks based on the concept of flow, derived from the continuous modeling of signal propagation across layers. We derive flow functions for fully connected, convolutional, and self-attention architectures, and we propose a new iterative pruning algorithm, Iterative Flow-Aware Pruning (IFAP), that leverages these measures to ...
Added: September 19, 2026
Dynamic Pattern Analysis: Method Overview and Trajectory Assessment of Object Development
Myachin A. L., Procedia Computer Science 2026 Vol. 287 P. 193–200
We extend the static pattern analysis method to the temporal dimension by introducing a six-type trajectory taxonomy that classifies objects according to the frequency and structure of pattern switches over an observation window of T > 8 periods. For each object, a reference pattern is designated as the most frequently occupied group over the observation ...
Added: September 18, 2026
A Bicriteria Fish War Game with Asymmetric Environmental Concern
Kuzyutin D., Smirnova N., Veselkov A., Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software 2026 Vol. 19 No. 3 P. 40–49
We consider spatial dynamic fishery management problem taking into account the resource migration process between an open-access fishing area and no-take marine protected area. The introduced extension of a standard single-criterion fish war game implies that each player aims to maximize simultaneously two performance criteria which present an economic benefit and an environmental conservation goal ...
Added: September 18, 2026
On the Efficiency of Bounded Multi-Source Shortest Path Algorithm
Громов Р. С., Нестеров Р.А., Proceedings of the Institute for System Programming of the RAS 2026 Vol. 38 No. 4 P. 23–44
This paper explores the performance criteria of the newest algorithm for solving the problem of finding shortest paths on a graph from a given vertex – Bounded Multi-Source Shortest Path Algorithm (BM-SSP). The algorithm was published in 2025 and, as its creators claim, it is asymptotically superior to Dijkstra’s deterministic algorithm. However, in the publication devoted ...
Added: September 18, 2026
Explainable Glaucoma Screening via Optic Disc Localization and Comparative Class Activation Map-Based Analysis
Ramos-Soto O., Perez-Zarate E., Ramos-Frutos J. et al., Machine Learning and Knowledge Extraction 2026 Vol. 8 No. 7 Article 173
Added: September 18, 2026
Четырехмерные гиперэллиптические многообразия, определяемые векторными раскрасками простых многогранников
Ероховец Николай Юрьевич, Математический сборник 2026 Т. 217 № 5 С. 45–89
Toric topology assigns to each simple convex n-polytope P with m facets an n-dimensional real moment-angle manifold RZP with a canonical action of Zm2=(Z/2Z)m. We consider (not necessarily free) actions of subgroups H⊂Zm2 on RZP. The orbit space N(P,H)=RZP/H carries an action of Zm2/H. For general n we introduce the notion of Hamiltonian C(n,k)-subcomplex in the boundary of an ...
Added: September 17, 2026
Explicit bases for Riemann-Roch spaces on elliptic curves and their application in constructing various elliptic code families
Kuninets A., Malygina E., Cryptography and Communications 2026
In this paper, we determine explicit bases for Riemann–Roch spaces associated with various families of elliptic codes. We establish the feasibility and provide exact algorithms for constructing bases of Riemann–Roch spaces corresponding to arbitrary divisors on elliptic curves, including the non-effective case. These results are subsequently applied to derive bases for quasi-cyclic elliptic codes and ...
Added: September 17, 2026
Робототехника и искусственный интеллект: международные практики и возможности для России, Дальнего Востока и Арктики
Кузнецов М. Е., Полякова М., Лукьянович В. et al., ФАНУ "Востокгосплан", 2026.
Обзор международных практик развития робототехники и искусственного интеллекта и оценка возможностей их применения в условиях России, в первую очередь для Дальнего Востока и Арктической зоны РФ ...
Added: September 16, 2026
Применение технологий компьютерного зрения в системах обработки визуальной информации
Lebedev O. B., Черкасов Р. И., Известия ЮФУ. Технические науки 2025 № 5(247) С. 254–276
This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are ...
Added: September 10, 2026
Модель глубокого обучения для автоматизированной интерпретации медицинских электрофизиологических данных
Lebedev O. B., Шмелева А. Г., Гежа Н. С., Информатика и автоматизация (Труды СПИИРАН) 2026 Т. 25 № 3 С. 720–750
This paper describes the development of a neural network model for automated analysis of medical data in electrophysiology based on deep learning methods. The relevance of this work stems from the growing need to improve the objectivity, speed, and accuracy of processing complex spatiotemporal signals, such as ECG or EEG. Convolutional neural networks (CNNs), which ...
Added: September 10, 2026
Automated detection of wolf howls using audio spectrogram transformers
Makarov N., Savchenko A., Zemtsova I. et al., Scientific Reports 2025 Vol. 15 Article 26641
The grey wolf (Canis lupus) is a pivotal species for ecological studies. As a key participant in ecosystem processes, it also serves as a model for investigating social structure formation and ecological adaptation. However, the species’ complex social behavior, spatial dynamics, and expansive habitats make monitoring and population assessments across large areas particularly challenging. In recent years, audio traps ...
Added: June 16, 2026
Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening
Vasilev R., Savchenko A., Blinov P. et al., Frontiers in Medicine 2026 Vol. 13 Article 1778404
Automated disease screening systems face challenges when applied to multi-class medical image analysis, particularly under severe class imbalance inherent in clinical datasets. Retinal fundus imaging enables non-invasive screening for multiple ocular and systemic diseases simultaneously, yet current automated systems typically assess risk for only a single pathology or a limited disease range. We developed a ...
Added: June 16, 2026
Approaches to the Detection of Deepfake in the Financial Organization’s Activities
Belov A. V., Fedotov G., , in: Proceedings of the 2025 INTERNATIONAL CONFERENCE "QUALITY MANAGEMENT, DIGITAL SECURITY, INFORMATION TECHNOLOGIES" (2025 QM&DS&IT).: IEEE, 2025. Ch. 1 P. 3–7.
In recent years, significant progress has been observed as content generated using AI technologies. In addition, tools regularly appear with which scammers can create a realistic fake content. Deepfake detection methods are currently actively used in the activities of financial organizations. With their help, a departments within financial organizations responsible for IT Security identify cases ...
Added: April 17, 2026
Online Neural Networks for Change-Point Detection
Hushchyn M., Arzymatov K., Derkach D., Machine Learning 2026 Vol. 115 Article 56
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear ...
Added: March 6, 2026
Image Modification Detections
Kseniia Prokudina, Mikhail Skriplyonok, Alexander Vostrikov, , in: 2026 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2026. P. 842–847.
This article analyzes the evolution of digital image manipulation detection methods over the 2016–2026 decade. It examines the transition from classic passive methods (ELA, metadata analysis, and noise pattern analysis) to deep neural network architectures (VGG16+U-Net, ManTra-Net, SPAN, and RDS-YOLOv5) and then to multimodal systems based on large language models (ForgeryGPT and FakeShield), which provide ...
Added: February 25, 2026
  • About
  • About
  • Key Figures & Facts
  • Sustainability at HSE University
  • Faculties & Departments
  • International Partnerships
  • Faculty & Staff
  • HSE Buildings
  • HSE University for Persons with Disabilities
  • Public Enquiries
  • Studies
  • Admissions
  • Programme Catalogue
  • Undergraduate
  • Graduate
  • Exchange Programmes
  • Summer University
  • Summer Schools
  • Semester in Moscow
  • Business Internship
  • Research
  • International Laboratories
  • Research Centres
  • Research Projects
  • Monitoring Studies
  • Conferences & Seminars
  • Academic Jobs
  • Yasin (April) International Academic Conference on Economic and Social Development
  • Media & Resources
  • Publications by staff
  • HSE Journals
  • Publishing House
  • iq.hse.ru: commentary by HSE experts
  • Library
  • Economic & Social Data Archive
  • Video
  • HSE Repository of Socio-Economic Information
  • HSE1993–2026
  • Contacts
  • Copyright
  • Privacy Policy
  • Site Map
Edit