HSE University and Yandex invite you to joint autumn school on generative models aimed at undergraduate/graduate students and young postdoctoral fellows from pure and applied mathematics. This intense four-day workshop will consist of 3 interdisciplinary mini-courses, list of invited talks, poster session by the participants and master-classes by industrial partners. Key topics of this school:
- Generative Adversarial Nets
- Statistical and Computational Optimal Transport
- Bayesian methods in machine learning
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" and organised by three laboratories of HSE University:
- 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.
- Optimization methods for optimal transport by Pavel Dvurechensky (WIAS Berlin)
- Generative models by Denis Derkach (HSE University)
- Introduction to scalable Bayesian methods by Dmitry Vetrov, Ekaterina Lobacheva (HSE University) and Nadia Chirkova (HSE University)
- Artem Babenko (Yandex, HSE)
- Evgeny Burnaev (Skoltech)
- Viktor Lempitsky (Skoltech)
- Ivan Oseledets (Skoltech)
- Maxim Panov (Skoltech, HSE)
- Vladimir Spokoiny (WIAS, HSE)
Poster Submission Guidelines:
Only posters of submitted and accepted abstracts will be offered presentation. Please note the following information for the preparation of your poster. Please bring your printed poster with you to the Conference.
- The poster layout is PORTRAIT.
- Please prepare your poster to fit the dimensions below. The poster can be prepared either on one sheet or few sheets of paper.
- The dimensions of the poster should not exceed 59.4 cm wide x 84 cm long (23.4 inches wide x 33.1 inches long). The recommended but not mandatory format is A1.
- Allocate the top of the poster for the title and authors as stated on the submitted abstract.
- Pins will be available for the mounting of posters.