4 citations · 5 across the 5 of their papers we have counts for
5 papers
Boltzmann Tuning of Generative Models
Victor Berger, Michele Sebag
The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The pr…
Anomaly Detection With Conditional Variational Autoencoders
Adrian Alan Pol, Victor Berger, Gianluca Cerminara +2
Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open rese…
Variational Auto-Encoder: not all failures are equal
Michele Sebag, Victor Berger, Michèle Sebag
We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distribution class used for the observation model.A first theoretical and experimental…
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
Victor Berger, Michèle Sebag
Conditional Generative Models are now acknowledged an essential tool in Machine Learning. This paper focuses on their control. While many approaches aim at disentangling the data t…
New Losses for Generative Adversarial Learning
Victor Berger, Michèle Sebag
Generative Adversarial Networks (Goodfellow et al., 2014), a major breakthrough in the field of generative modeling, learn a discriminator to estimate some distance between the tar…