22k citations · 25.1k across the 40 of their papers we have counts for
7 papers · 1 filter
Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski +2
Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is stil…
Wasserstein Auto-Encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly +1
We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance…
Toward Optimal Run Racing: Application to Deep Learning Calibration
Olivier Bousquet, Sylvain Gelly, Karol Kurach +4
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural arc…
Critical Hyper-Parameters: No Random, No Cry
Olivier Bousquet, Sylvain Gelly, Karol Kurach +2
The selection of hyper-parameters is critical in Deep Learning. Because of the long training time of complex models and the availability of compute resources in the cloud, "one-sho…
Better Text Understanding Through Image-To-Text Transfer
Karol Kurach, Sylvain Gelly, Michal Jastrzebski +4
Generic text embeddings are successfully used in a variety of tasks. However, they are often learnt by capturing the co-occurrence structure from pure text corpora, resulting in li…
From optimal transport to generative modeling: the VEGAN cookbook
Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin +2
We study unsupervised generative modeling in terms of the optimal transport (OT) problem between true (but unknown) data distribution and the latent variable model distributi…
AdaGAN: Boosting Generative Models
Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet +2
Generative Adversarial Networks (GAN) (Goodfellow et al., 2014) are an effective method for training generative models of complex data such as natural images. However, they are not…