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.
It's this time of a year: once again students from all over the world gathered in Moscow to participate in the Deep|Bayes 2019 – a summer school on Bayesian Deep Learning. Just like the last year, the school featured both lectures and practical assignments. We've been also fortunate to have a couple of invited speakers: Novi Quadrianto from University of Sussex and Higher School of Economics, Maurizio Filippone from EURECOM, Francisco Jesus Rodriguez Ruiz from Columbia University and University of Cambridge, Andrey Malinin from University of Cambridge and Sergey Bartunov from DeepMind.
We are looking for a postdoc to join our group! Please see details here.
One again, we're organizing an international summer school on Bayesian Deep Learning to be held in Moscow, August 20–25. Head over to deepbayes.ru to view last year's videos, practical assignments and apply to this year's run.
We got 3 papers accepted to ICLR 2019:
- Variational Autoencoder with Arbitrary Conditioning by Oleg Ivanov, Michael Figurnov and Dmitry Vetrov;
- Variance Networks: When Expectation Does Not Meet Your Expectations by Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov;
- The Deep Weight Prior by Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitriy Vetrov in collaboration with Max Welling.
This year's NeurIPS conference turned out to be a very fruitful one! We've had
- Two papers accepted, one of them being a spotlight
- Quantifying Learning Guarantees for Convex but Inconsistent Surrogates by Kirill Struminsky and Anton Osokin in collaboration with Simon Lacoste-Julien
- Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs by Timur Garipov, Dmitrii Podoprikhin and Dmitry Vetrov in collaboration with Pavel Izmailov and Andrew Gordon Wilson
- An invited talk by Dmitry Vetrov at the Bayesian Deep Learning workshop
- Three papers accepted to the aforementioned workshop
- Importance Weighted Hierarchical Variational Inference by Artem Sobolev and Dmitry Vetrov
- Variational Dropout via Empirical Bayes by Valery Kharitonov, Dmitry Molchanov and Dmitry Vetrov
- Subset-Conditioned Generation Using Variational Autoencoder With A Learnable Tensor-Train Induced Prior by Maksim Kuznetsov, Daniil Polykovskiy and Dmitry Vetrov in collaboration with Alexander Zhebrak
- A paper accepted to the Reinforcement Learning under Partial Observability workshop (contributed talk)
- Joint Belief Tracking and Reward Optimization through Approximate Inference by Pavel Shvechikov, Alexander Grishin, Arseny Kuznetsov, Alexander Fritzler and Dmitry Vetrov
- A paper accepted to the Compact Deep Neural Network Representation with Industrial Applications workshop
- Bayesian Sparsification of Gated Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova and Dmitry Vetrov
- Two papers accepted, one of them being a spotlight
We have 3 papers accepted to ACML 2018: Concorde: Morphological Agreement in Conversational Models by Daniil Polykovskiy in collaboration with Dmitry Soloviev and Sergey Nikolenko, ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks by Iurii Kemaev, Daniil Polykovskiy and Dmitry Vetrov, and Extracting Invariant Features From Images Using An Equivariant Autoencoder by Daniil Polykovskiy in collaboration with Denis Kuzminykh, Alexander Zhebrak.
Daniil Polykovskiy and Dmitry Vetrov in collaboration with Insilico Medicine (Alexander Zhebrak, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, and Artur Kadurin) have published "Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery" paper in the Molecular Pharmaceutics journal where they applied modern Deep Learning techniques to the problem of molecules generation.
We're happy to inform that our group got two papers accepted to NIPS 2018: "Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs" (spotlight talk) by Timur Garipov*, Dmitrii Podoprikhin*, Dmitry Vetrov in collaboration with Pavel Izmailov* (our alumni) and Andrew Gordon Wilson from Cornell University (* denotes equal contribution) and "Quantifying Learning Guarantees for Convex but Inconsistent Surrogates" by Kirill Struminsky and Anton Osokin (equal contribution) in collaboration with Simon Lacoste-Julien from University of Montreal.
Also, our reviewers' contribution has been recognized: Anton Osokin made it into top-200 reviewers, and Dmitry Vetrov got in top 30%.
Over the course of last week over a hundred students from all over the world gathered in Moscow to familiarize themselves with modern research on Bayesian Deep Learning. The school presented an intense in-depth immersion through both lectures and practical assignments. The lecturers included not only the members of our group, but some prominent invited speakers as well: Max Welling from University of Amsterdam, Maurizio Filippone from EURECOM, Alessandro Achille from University of California, Los Angeles, Sergey Bartunov and Michael Figurnov from DeepMind.
The slides, videos and practicals are available at deepbayes.ru