Alexander Shabalin
- Research Assistant:Faculty of Computer Science / AI and Digital Science Institute / Centre of Deep Learning and Bayesian Methods
- Doctoral Student:Faculty of Computer Science / Big Data and Information Retrieval School
- Alexander Shabalin has been at HSE University since 2020.
Education
- 2023
Master's in Applied Mathematics and Information Science
HSE University - 2021
Bachelor's in Applied Mathematics and Information Science
HSE University
Postgraduate Studies
1st year of study
Approved topic of thesis: Application of Denoising Diffusion Probabilistic Models for Text Data
Academic Supervisor: Vetrov, Dmitry
Courses (2023/2024)
- Deep Learning for Natural Language Processing (Bachelor’s programme; Faculty of Computer Science; 4 year, 1, 2 module)Rus
- Past Courses
Courses (2022/2023)
- Machine Learning 1 (Bachelor’s programme; Faculty of Computer Science; 3 year, 1, 2 module)Rus
Machine Learning 2 (Bachelor’s programme; Faculty of Computer Science; field of study "01.03.02. Прикладная математика и информатика", field of study "01.03.02. Прикладная математика и информатика"; 3 year, 3, 4 module)Rus
- Self-supervised Learning (Bachelor’s programme; Faculty of Computer Science; 4 year, 3 module)Rus
Courses (2021/2022)
- How to Win a Data Science Competition: Learn from Top Kagglers (Bachelor’s programme; Faculty of Computer Science; 4 year, 2, 3 module)Eng
- Machine Learning 1 (Bachelor’s programme; Faculty of Computer Science; 3 year, 1, 2 module)Rus
Machine Learning 2 (Bachelor’s programme; Faculty of Computer Science; field of study "01.03.02. Прикладная математика и информатика", field of study "01.03.02. Прикладная математика и информатика"; 3 year, 3, 4 module)Rus
Publications2
- Preprint Shabalin A., Meshchaninov V., Бадмаев Т. М., Molchanov D., Бартош Г. С., Markov S., Vetrov D. TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings / Cornell University. Series Computer Science "arxiv.org". 2023. doi
- Chapter Alexander Shabalin, Sadrtdinov I., Evgeniy Shabalin. [Re]“Towards Understanding Grokking”, in: ML Reprobucibility Challenge 2022. , 2023. doi doi (in press)