• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

‘Every Article on NeurIPS Is Considered a Significant Result’

‘Every Article on NeurIPS Is Considered a Significant Result’

© iStock

Staff members of the HSE Faculty of Computer Science will present 12 of their works at the 37th Conference and Workshop on Neural Information Processing Systems (NeurIPS), one of the most significant events in the field of artificial intelligence and machine learning. This year it will be held on December 10–16 in New Orleans (USA).

In 2023, NeurIPS reviewers received over 13,000 articles for consideration—fewer than 4,000 of which were selected for presentation at the conference. Twelve articles by researchers of the Faculty of Computer Science are among the selected papers.

Full list of articles by HSE FCS staff members at NeurIPS

Next 

The article ‘Entropic Neural Optimal Transport via Diffusion Processes’, prepared with the participation of research professor Dmitry Vetrov, will be one of 77 selected reports to be presented at the conference.

Alexey Naumov

‘Every article on NeurIPS is considered a significant result, which is something sought by research teams around the world. The work of our faculty resulted in 12 articles—this is a reason for us to feel proud. Such appreciation of our work confirms the highest level of research conducted by the staff of the Faculty of Computer Science. The topics of this year's articles feature large language models, reinforcement learning, optimisation and many other relevant scientific issues,’ says Alexey Naumov, Head of the International Laboratory of Stochastic Algorithms and High-Dimensional Inference.

Darina Dvinskikh, Associate Professor at the Big Data and Information Retrieval School, and Ildus Sadrtdinov, Research Assistant at the Centre of Deep Learning and Bayesian Methods, spoke about their research.

Darina Dvinskikh

'We considered the problem of minimising a non-smooth stochastic function under the assumption that instead of gradient information, access is available only to implementations of the values of the objective function, possibly noisy ones. The main motivation for considering such a gradient-free oracle are its various applications in medicine, biology and physics, where the objective function can be calculated only through numerical modelling or as a result of a real experiment, which makes it impossible to use automatic differentiation.

In the paper, we proposed an algorithm that is optimal in terms of oracle complexity, iterative complexity and the maximum level of acceptable noise (possibly adversarial). The new algorithm converges under less restrictive assumptions than the existing optimal algorithm. Therefore, the proposed algorithm can be applied to a wider class of problems in which noise can have heavy tails.'

Ildus Sadrtdinov

'In our article, we explore how to most effectively ensemble neural networks in transfer learning. The task appears to be complex due to the fact that usually only one pre-trained model is available, and the neural networks that we train with it produce similar predictions. It results in the degradation of the ensemble quality.

In this paper, we show that the existing methods of ensembling can be improved for knowledge transfer. We propose our own modification of one of the methods that better corresponds to transfer learning setup. Along the way, we develop additional intuition about how loss landscape function works when we retrain the pre-trained model with new data.'

See also:

‘We Bring Together the Best Russian Scientists and AI Researchers at HSE University Site’

On October 25–26, 2024, HSE University’s AI and Digital Science Institute and the AI Research Centre hold the Fall into ML 2024 conference in Moscow. This year’s event will focus on the prospects in development of fundamental artificial intelligence, with SBER as its conference title partner.

HSE Researchers Demonstrate Effectiveness of Machine Learning in Forecasting Inflation

Inflation is a key indicator of economic stability, and being able to accurately forecast its levels across regions is crucial for governments, businesses, and households. Tatiana Bukina and Dmitry Kashin at HSE Campus in Perm have found that machine learning techniques outperform traditional econometric models in long-term inflation forecasting. The results of the study focused on several regions in the Privolzhskiy Federal District have been published in HSE Economic Journal.

‘The Goal of the Spring into ML School Is to Unite Young Scientists Engaged in Mathematics of AI’

The AI and Digital Science Institute at the HSE Faculty of Computer Science and Innopolis University organised a week-long programme for students, doctoral students, and young scientists on the application of mathematics in machine learning and artificial intelligence. Fifty participants of Spring into ML attended 24 lectures on machine learning, took part in specific pitch sessions, and completed two mini-courses on diffusion models—a developing area of AI for data generation.

Software for Rapid Detection of Dyslexia Developed in Russia

HSE scientists have developed a software tool for assessing the presence and degree of dyslexia in school students based on their gender, age, school grade, and eye-tracking data. The application is expected to be introduced into clinical practice in 2024. The underlying studies were conducted by specialists in machine learning and neurolinguistics at the HSE AI Research Centre.

‘In the Future, I Expect Rapid Development of Professions Related to Prompt Engineering’

The English-language programme of HSE Online ‘Master of Computer Vision’ will change its name to ‘Artificial Intelligence and Computer Vision’ in 2024. Andrey Savchenko, the programme academic supervisor, shares how the new name will affect the programme semantics, why AI has become the main federal trend in the field of information technology, and what tasks graduates will solve.

Artificial Intelligence as a Driver of Digital Transformation

In December, the HSE Institute for Statistical Studies and Economics of Knowledge and the HSE AI Research Centre participated in UNCTAD eWeek to discuss the future of the emerging digital economy. One of the topics discussed during the conference was artificial intelligence and its applications in driving the digital transformation of industry sectors. The session was co-organised by HSE University.

HSE University Receives Highest Grant under Priority 2030 Programme

HSE University has proved its leading position in the first group of the ‘Research Leadership’ field under the Priority 2030 programme. The university has also received the highest grant for teaching digital competencies to students, demonstrating its educational leadership in the fields of digital technologies and AI.

‘The Future Lies with AI Technologies and HSE University Understands That’

At the AI Journey 2023 international conference in Moscow, a ranking of Russian universities that train the best AI specialists was published. HSE University entered the A+ leadership group, taking first place according to such criteria as ‘Demand for hiring graduates’, ‘Quality of educational environment’, and ‘Activities for the development of school education’. Ivan Arzhantsev, Dean of HSE University’s Faculty of Computer Science, spoke to the HSE News Service about how AI specialists are trained at HSE University and what plans the university has in this area.

Specialists from the HSE Institute of Education Confirm GigaChat’s Erudition in Social Sciences

A multimodal neural network model by Sber, under the supervision of HSE University’s expert commission, has successfully passed the Unified State Exam in social studies. GigaChat completed all exam tasks and scored 67 points.

HSE University Students Win in the AIJ Science Competition at AI Journey 2023

The International Sber Conference of Artificial Intelligence, ‘AI Journey 2023’ recently took place in Moscow. Alexander Rogachev, doctoral student of the HSE Faculty of Computer Science, and Egor Egorov, an HSE 4th-year undergraduate student became the winners of the AIJ Science competition for scientific articles on artificial intelligence that was held as part of the event. The research was carried out under the umbrella of the HSE's Laboratory of Methods for Big Data Analysis (LAMBDA).