Your vit is secretly a hybrid discriminative-generative diffusion model
Diffusion Denoising Probability Models (DDPM) and Vision Transformer (ViT) have
demonstrated significant progress in generative tasks and discriminative tasks, respectively …
demonstrated significant progress in generative tasks and discriminative tasks, respectively …
Energy-based models for anomaly detection: A manifold diffusion recovery approach
We present a new method of training energy-based models (EBMs) for anomaly detection
that leverages low-dimensional structures within data. The proposed algorithm, Manifold …
that leverages low-dimensional structures within data. The proposed algorithm, Manifold …
End-to-end stochastic optimization with energy-based model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems
that involve unknown parameters. By integrating predictive modeling with an implicitly …
that involve unknown parameters. By integrating predictive modeling with an implicitly …
On sampling with approximate transport maps
Transport maps can ease the sampling of distributions with non-trivial geometries by
transforming them into distributions that are easier to handle. The potential of this approach …
transforming them into distributions that are easier to handle. The potential of this approach …
Energy-guided entropic neural optimal transport
Energy-based models (EBMs) are known in the Machine Learning community for decades.
Since the seminal works devoted to EBMs dating back to the noughties, there have been a …
Since the seminal works devoted to EBMs dating back to the noughties, there have been a …
Explaining the effects of non-convergent sampling in the training of Energy-Based Models
In this paper, we quantify the impact of using non-convergent Markov chains to train Energy-
Based models (EBMs). In particular, we show analytically that EBMs trained with non …
Based models (EBMs). In particular, we show analytically that EBMs trained with non …
Revisiting energy based models as policies: Ranking noise contrastive estimation and interpolating energy models
A crucial design decision for any robot learning pipeline is the choice of policy
representation: what type of model should be used to generate the next set of robot actions …
representation: what type of model should be used to generate the next set of robot actions …
Explaining the effects of non-convergent MCMC in the training of Energy-Based Models
In this paper, we quantify the impact of using non-convergent Markov chains to train Energy-
Based models (EBMs). In particular, we show analytically that EBMs trained with non …
Based models (EBMs). In particular, we show analytically that EBMs trained with non …
Fast and functional structured data generators rooted in out-of-equilibrium physics
In this study, we address the challenge of using energy-based models to produce high-
quality, label-specific data in complex structured datasets, such as population genetics, RNA …
quality, label-specific data in complex structured datasets, such as population genetics, RNA …
Latent space energy-based model for fine-grained open set recognition
Fine-grained open-set recognition (FineOSR) aims to recognize images belonging to
classes with subtle appearance differences while rejecting images of unknown classes. A …
classes with subtle appearance differences while rejecting images of unknown classes. A …