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.
Mini-courses:
- 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)
Invited talks:
- 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.
School materials:
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".
