Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Variational Perspective on Diffusion-Based Generative Models and Score
  Matching   Discrete-time diffusion-based generative models and score matching methods
have shown promising results in modeling high-dimensional image data. Recently,
Song et al. (2021) show that diffusion processes that transform data into noise
can be reversed via learning the score function, i.e. the gradient of the
log-density of the perturbed data. They propose to plug the learned score
function into an inverse formula to define a generative diffusion process.
Despite the empirical success, a theoretical underpinning of this procedure is
still lacking. In this work, we approach the (continuous-time) generative
diffusion directly and derive a variational framework for likelihood
estimation, which includes continuous-time normalizing flows as a special case,
and can be seen as an infinitely deep variational autoencoder. Under this
framework, we show that minimizing the score-matching loss is equivalent to
maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed
by Song et al. (2021), bridging the theoretical gap.

28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th