29 citations · 73 across the 10 of their papers we have counts for
10 papers
The implicated scientist: on the role of AI researchers in the development of weapons systems
Alexandra Volokhova, Alex Hernandez-Garcia
Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involved in mass killings and destruction at sc…
Irresponsible AI: big tech's influence on AI research and associated impacts
Alex Hernandez-Garcia, Alexandra Volokhova, Ezekiel Williams +2
The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field. This trend has been…
Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova, Léna Néhale Ezzine, Piotr Gaiński +5
Generating stable molecular conformations is crucial in several drug discovery applications, such as estimating the binding affinity of a molecule to a target. Recently, generative…
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova, Michał Koziarski, Alex Hernández-García +7
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampl…
Crystal-GFN: sampling crystals with desirable properties and constraints
Mila AI4Science, :, Alex Hernandez-Garcia +11
The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challe…
A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos +6
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A…
Generative Flow Networks for Discrete Probabilistic Modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu +3
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Building upon the theory of generative flow…
Stochasticity in Neural ODEs: An Empirical Study
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. Despite its success, continuous-time…
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha +2
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint dist…
Cherenkov Detectors Fast Simulation Using Neural Networks
Denis Derkach, Nikita Kazeev, Fedor Ratnikov +2
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level…