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June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
June 2, 2026
Discovering Science through Russian Language: HSE Prep Year Students Present at International Conference in Kazan
On May 23, 2026, the V International Scientific and Practical Conference ‘Discovering the World of Science’ took place in Kazan at the Preparatory Faculty for International Students of Kazan Federal University. Four students of the HSE International Preparatory Year took part in the event: two delivered their presentations in person, while two participated online. Their work was supervised by Acting Director of the International Prep Year Irina Isaeva and lecturer Ekaterina Kozhemyakova.
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.

 

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Towards Automatic Manipulation of Arbitrary Structures in Connectivist Paradigm with Tensor Product Variable Binding

P. 375–383.
Demidovskij A.

Building a bridge between symbolic and connectionist level of computations requires constructing a full pipeline that accepts symbolic structures as an input, translates them to distributed representation, performs manipulations with this representation equivalent to symbolic manipulations and translates it back to the symbolic structure. This work proposes neural architecture that is capable of joining two structures which is an essential part of structure manipulation step in the connectionist pipeline. Verification of the architecture demonstrates scalability of the solution, a set of advice for engineering practitioners was elaborated.

Language: English
Full text
DOI
Keywords: artificial neural networksUnsupervised learningtensor computationsConnectionism

In book

Advances in Neural Computation, Machine Learning, and Cognitive Research III
Springer, 2020.
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