HSE University and Yandex invite you to the joint second autumn school on generative models aimed at young undergraduate/graduate students planning to develop research career. This intense four-day workshop will consist of 3 interdisciplinary mini-courses, list of invited talks and round table discussion with the leading researchers. Key topics of this school are:
- Introduction to Deep Learning
- Generative Models for fun and profit
- Introduction to bandit problems
Due to pandemic, the school is going to run in the mixed format: in addition to a zoom meeting room, there is a physical room booked that would allow participants to follow the event from HSE Pokrovsky campus.
The school is organised by three HSE laboratories:
- LAMBDA (Laboratory of Methods for Big Data Analysis)
- DeepBayes (Centre of Deep Learning and Bayesian Methods)
- HDI Lab (International laboratory of stochastic algorithms and high-dimensional inference)
Yandex is an industry partner of the school.
- Introduction to Machine Learning by Dmitry Vetrov, Ekaterina Lobacheva (HSE University) and Nadia Chirkova (HSE University)
- Generative models by Denis Derkach (HSE University)
- Introduction to few-shot learning by Quentin Paris (HSE University)
- Alexey Artemov (Skoltech)
- Evgeny Burnaev (Skoltech)
- Anton Konushin (MSU, HSE, YSDA)
- Stanislav Morozov (Yandex)
- Maxim Panov (Skoltech)
- Evgenii Stepanov (PDMI RAS, HSE)
- Alexandra Suvorikova (WIAS, HSE)
- Nikita Zhivotovsky (Google, HSE)
Live discussion with leading researchers:
On the 27th afternoon, we plan to have a live panel discussion with leading scientists on the future of AI and research career.
All materials of the school is available via this page timetable. It contains slides of the lectures and talks. All seminar materials are available via github repository: https://github.com/HSE-LAMBDA/GenModels-2020
This school is supported by the RSF grant N19-71-30020 "Applications of probabilistic artificial neural generative models to development of digital twin technology for Non-linear stochastic systems".