no code implementations • 20 Oct 2023 • Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
1 code implementation • 7 Oct 2023 • Mila AI4Science, Alex Hernandez-Garcia, Alexandre Duval, Alexandra Volokhova, Yoshua Bengio, Divya Sharma, Pierre Luc Carrier, Yasmine Benabed, Michał Koziarski, Victor Schmidt
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
1 code implementation • 30 Jan 2023 • Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.
2 code implementations • 3 Feb 2022 • Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.
1 code implementation • ICLR Workshop DeepDiffEq 2019 • Viktor Oganesyan, Alexandra Volokhova, Dmitry Vetrov
Stochastic regularization of neural networks (e. g. dropout) is a wide-spread technique in deep learning that allows for better generalization.
3 code implementations • 1 May 2019 • Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry Vetrov
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning.
no code implementations • 28 Mar 2019 • Denis Derkach, Nikita Kazeev, Fedor Ratnikov, Andrey Ustyuzhanin, Alexandra Volokhova
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details.