15 citations · 22 across the 5 of their papers we have counts for
8 papers · 1 filter
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
Payel Das, Tom Sercu, Kahini Wadhawan +12
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…
Sobolev Independence Criterion
Youssef Mroueh, Tom Sercu, Mattia Rigotti +2
We propose the Sobolev Independence Criterion (SIC), an interpretable dependency measure between a high dimensional random variable X and a response variable Y . SIC decomposes to…
Wasserstein Barycenter Model Ensembling
Pierre Dognin, Igor Melnyk, Youssef Mroueh +3
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Was…
Sobolev Descent
Youssef Mroueh, Tom Sercu, Anant Raj
We study a simplification of GAN training: the problem of transporting particles from a source to a target distribution. Starting from the Sobolev GAN critic, part of the gradient…
Adversarial Semantic Alignment for Improved Image Captions
Pierre L. Dognin, Igor Melnyk, Youssef Mroueh +2
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignme…
Semi-Supervised Learning with IPM-based GANs: an Empirical Study
Tom Sercu, Youssef Mroueh
We present an empirical investigation of a recent class of Generative Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their performance for semi-supervised…