News

  1. NeurIPS 2020

    The paper On Power Laws in Deep Ensembles by Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan and Dmitry Vetrov has been aceepted to NeurIPS 2020 for a spotlight presentation.

  2. Dmitry Vetrov joins the ELLIS Society

    Dmitry Vetrov is the first Russian scientist to be elected as a member to the ELLIS Society (the European Laboratory for Learning and Intelligent Systems), a leading European organization in the field of artificial intelligence.

    Being a member of the ELLIS Society, Dmitry Vetrov says, will provide more opportunities for students and doctoral students in the faculty.

  3. ICML 2020

    Two papers have been accepted to the conference track of ICML 2020:

    And the paper "On Power Laws in Deep Ensembles" by Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, and Dmitry Vetrov has been accepted to the Workshop on Uncertainty and Robustness in Deep Learning, ICML 2020

  4. Anton Osokin became the winner of the Moscow Government Young Scientist Prize for 2019

    The prize was awarded for a cycle of papers on machine learning methods for predicting structured objects. The cycle includes 13 works of 2011-2019 published at leading international conferences such as NeurIPS, ICML, CVPR, ICCV, ICLR and in the leading journals IEEE TPAMI and IJCV.

  5. NeurIPS 2019 Workshops

    We've got several papers accepted to NeurIPS workshops:

    1. "Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning" by Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov and Dmitry Vetrov has been accepted to the Bayesian Deep Learning Workshop.
    2. "Low-variance Gradient Estimates for the Plackett-Luce Distribution" by Artyom Gadetsky, Kirill Struminsky, Novi Quadrianto and Dmitry Vetrov in collaboration with Christopher Robinson has been accepted to the Bayesian Deep Learning Workshop.
    3. "Unsupervised Domain Adaptation with Shared Latent Dynamics for Reinforcement Learning" by Evgenii Nikishin, Arsenii Ashukha and Dmitry Vetrov has also been accepted to the Bayesian Deep Learning Workshop.
    4. "Structured Sparsification of Gated Recurrent Neural Networks" by Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich and Dmitry Vetrov has been accepted to the workshop on Context and Compositionality in Biological and Artificial Neural Systems.
    5. Finally, Max Kochurov contributed to the "PyMC4: Exploiting Coroutines for Implementing a Probabilistic Programming Framework" paper accepted to the workshop on Program Transformations.
  6. NeurIPS 2019

    This year we've doubled our presence at NeurIPS with four papers accepted:

    1. Importance Weighted Hierarchical Variational Inference by Artem Sobolev and Dmitry Vetrov.
    2. The Implicit Metropolis-Hastings Algorithm by Kirill Neklyudov and Dmitry Vetrov in collaboration with Evgenii Egorov.
    3. A Simple Baseline for Bayesian Uncertainty in Deep Learning by Timur Garipov and Dmitry Vetrov in collaboration with Wesley Maddox, Pavel Izmailov and Andrew Gordon Wilson.
    4. A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models by Maxim Kuznetsov, Daniil Polykovskiy and Dmitry Vetrov in collaboration with Alexander Zhebrak.

    Good academic service is not only about producing novel research, but also about providing critical assessment of other's work. We're proud that Kirill Struminsky, Ekaterina Lobacheva, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov and Dmitry Kropotov were recognized as top 50% reviewers.

  7. A paper published in Nature Biotechnology

    Insilico Medicine published an article in Nature Biotechnology coauthored by our members Maxim Kuznetsov and Daniil Polykovskiy. The paper describes a timed challenge, where the new machine learning system called Generative Tensorial Reinforcement Learning (GENTRL) designed six novel inhibitors of DDR1, a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.

← Newer Page 2 out of 3 Older →