Search Results for author:
Found 6 papers, 0 papers with code
Boltzmann Tuning of Generative Models
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.
Boltzman Tuning of Generative Models
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.
Anomaly Detection With Conditional Variational Autoencoders
Exploiting the rapid advances in probabilistic inference, in particular variational Bayes and variational autoencoders (VAEs), for anomaly detection (AD) tasks remains an open research question.
Variational Auto-Encoder: not all failures are equal
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 contribution of the paper is to establish that even in the large sample limit with arbitrarily powerful neural architectures and latent space, the VAE failsif the sharpness of the distribution class does not match the scale of the data. Our second claim is that the distribution sharpness must preferably be learned by the VAE (as opposed to, fixed and optimized offline): Autonomously adjusting this sharpness allows the VAE to dynamically control the trade-off between the optimization of the reconstruction loss and the latent compression.
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
This paper focuses on their control.
New Losses for Generative Adversarial Learning
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 target and the candidate distributions.
