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Found 9 papers, 3 papers with code
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
We consider the linear discriminant analysis problem in the high-dimensional settings.
Diffusion Schrödinger Bridge Matching
However, while it is desirable in many applications to approximate the deterministic dynamic Optimal Transport (OT) map which admits attractive properties, DDMs and FMMs are not guaranteed to provide transports close to the OT map.
From Denoising Diffusions to Denoising Markov Models
We propose a unifying framework generalising this approach to a wide class of spaces and leading to an original extension of score matching.
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
We then provide two complementary theoretical analyses of the VR-IWAE bound and thus of the standard IWAE bound.
Conditional Simulation Using Diffusion Schrödinger Bridges
We extend the Schr\"odinger bridge framework to conditional simulation.
On PAC-Bayesian reconstruction guarantees for VAEs
Despite its wide use and empirical successes, the theoretical understanding and study of the behaviour and performance of the variational autoencoder (VAE) have only emerged in the past few years.
Online Variational Filtering and Parameter Learning
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data.
Learning to Defense by Learning to Attack
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
Learning to Defend by Learning to Attack
Adversarial training provides a principled approach for training robust neural networks.
