15 citations · 24 across the 2 of their papers we have counts for
6 papers
Tabular Transformers for Modeling Multivariate Time Series
Inkit Padhi, Yair Schiff, Igor Melnyk +6
Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock t…
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis
Adam Block, Youssef Mroueh, Alexander Rakhlin +1
Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dyna…
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Mingrui Liu, Youssef Mroueh, Jerret Ross +4
Adaptive gradient algorithms perform gradient-based updates using the history of gradients and are ubiquitous in training deep neural networks. While adaptive gradient methods theo…
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Mingrui Liu, Wei Zhang, Youssef Mroueh +4
Generative Adversarial Networks (GANs) are a powerful class of generative models in the deep learning community. Current practice on large-scale GAN training utilizes large models…
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…
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…