Tag "neural networks"

Researchers at HSE University and the AIRI Institute have proposed a method for quickly fine-tuning neural networks. Their approach involves processing data in groups and then optimally shuffling these groups to improve their interactions. The method outperforms alternatives in image generation and analysis, as well as in fine-tuning text models, all while requiring less memory and training time. The results have been presented at the NeurIPS 2024 Conference.

Egor Churaev specialises in neural networks. In an interview for the HSE Young Scientists project, he talked about his program for determining the emotions and engagement of online conference participants, his trip to Brazil, and his sports hobbies.

Researchers from theHSE AI Research Centre, AIRI, and the University of Bremen have developed a new image editing method based on deep learning—StyleFeatureEditor. This tool allows for precise reproduction of even the smallest details in an image while preserving them during the editing process. With its help, users can easily change hair colour or facial expressions without sacrificing image quality. The results of this three-party collaboration were published at the highly-cited computer vision conference CVPR 2024.

Researchers at HSE University, MISiS National University of Science and Technology, and the Artificial Intelligence Research Institute (AIRI) have developed an enhanced approach to reinforcement learning for neural networks tasked with navigation in three-dimensional environments. By using the attention mechanism, they managed to improve the performance of a graph neural network by 15%. The study results have been published in IEEE Access.

Specialists at HSE Campus in Perm and clinicians at Perm City Clinical Hospital No. 4, have been collaborating to develop a neural network capable of determining the root cause of a stroke. This marks the world's first attempt to create such a system, the developers note.

A group of scientists from the HSE Faculty of Computer Science has conducted a study on the ability of neural networks to detect humour. It turns out that for more reliable recognition, it’s necessary to change the approach to creating datasets on which neural networks are trained. The scientists presented these results at one of the world's most important conferences on natural language processing — EMNLP 2023.