2,403 research outputs found
Auxiliary Guided Autoregressive Variational Autoencoders
Generative modeling of high-dimensional data is a key problem in machine
learning. Successful approaches include latent variable models and
autoregressive models. The complementary strengths of these approaches, to
model global and local image statistics respectively, suggest hybrid models
that encode global image structure into latent variables while autoregressively
modeling low level detail. Previous approaches to such hybrid models restrict
the capacity of the autoregressive decoder to prevent degenerate models that
ignore the latent variables and only rely on autoregressive modeling. Our
contribution is a training procedure relying on an auxiliary loss function that
controls which information is captured by the latent variables and what is left
to the autoregressive decoder. Our approach can leverage arbitrarily powerful
autoregressive decoders, achieves state-of-the art quantitative performance
among models with latent variables, and generates qualitatively convincing
samples.Comment: Published as a conference paper at ECML-PKDD 201
Tensor Monte Carlo: particle methods for the GPU era
Multi-sample, importance-weighted variational autoencoders (IWAE) give
tighter bounds and more accurate uncertainty estimates than variational
autoencoders (VAE) trained with a standard single-sample objective. However,
IWAEs scale poorly: as the latent dimensionality grows, they require
exponentially many samples to retain the benefits of importance weighting.
While sequential Monte-Carlo (SMC) can address this problem, it is
prohibitively slow because the resampling step imposes sequential structure
which cannot be parallelised, and moreover, resampling is non-differentiable
which is problematic when learning approximate posteriors. To address these
issues, we developed tensor Monte-Carlo (TMC) which gives exponentially many
importance samples by separately drawing samples for each of the latent
variables, then averaging over all possible combinations. While the sum
over exponentially many terms might seem to be intractable, in many cases it
can be computed efficiently as a series of tensor inner-products. We show that
TMC is superior to IWAE on a generative model with multiple stochastic layers
trained on the MNIST handwritten digit database, and we show that TMC can be
combined with standard variance reduction techniques
Resampled Priors for Variational Autoencoders
We propose Learned Accept/Reject Sampling (LARS), a method for constructing
richer priors using rejection sampling with a learned acceptance function. This
work is motivated by recent analyses of the VAE objective, which pointed out
that commonly used simple priors can lead to underfitting. As the distribution
induced by LARS involves an intractable normalizing constant, we show how to
estimate it and its gradients efficiently. We demonstrate that LARS priors
improve VAE performance on several standard datasets both when they are learned
jointly with the rest of the model and when they are fitted to a pretrained
model. Finally, we show that LARS can be combined with existing methods for
defining flexible priors for an additional boost in performance
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