2 research outputs found
Learning Generative Models using Denoising Density Estimators
Learning probabilistic models that can estimate the density of a given set of
samples, and generate samples from that density, is one of the fundamental
challenges in unsupervised machine learning. We introduce a new generative
model based on denoising density estimators (DDEs), which are scalar functions
parameterized by neural networks, that are efficiently trained to represent
kernel density estimators of the data. Leveraging DDEs, our main contribution
is a novel technique to obtain generative models by minimizing the
KL-divergence directly. We prove that our algorithm for obtaining generative
models is guaranteed to converge to the correct solution. Our approach does not
require specific network architecture as in normalizing flows, nor use ordinary
differential equation solvers as in continuous normalizing flows. Experimental
results demonstrate substantial improvement in density estimation and
competitive performance in generative model training.Comment: Code and models available at
https://drive.google.com/file/d/1EzKRxnFG1Hd8g6Ggvt-jvKkgpDDwK2b
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intractable
to compute the entropy of the distribution of an arbitrary continuous random
variable. In this paper, we propose the amortized residual denoising
autoencoder (AR-DAE) to approximate the gradient of the log density function,
which can be used to estimate the gradient of entropy. Amortization allows us
to significantly reduce the error of the gradient approximator by approaching
asymptotic optimality of a regular DAE, in which case the estimation is in
theory unbiased. We conduct theoretical and experimental analyses on the
approximation error of the proposed method, as well as extensive studies on
heuristics to ensure its robustness. Finally, using the proposed gradient
approximator to estimate the gradient of entropy, we demonstrate
state-of-the-art performance on density estimation with variational
autoencoders and continuous control with soft actor-critic.Comment: accepted in ICML 202