1,328 research outputs found
Adaptive Density Estimation for Generative Models
Unsupervised learning of generative models has seen tremendous progress over
recent years, in particular due to generative adversarial networks (GANs),
variational autoencoders, and flow-based models. GANs have dramatically
improved sample quality, but suffer from two drawbacks: (i) they mode-drop,
i.e., do not cover the full support of the train data, and (ii) they do not
allow for likelihood evaluations on held-out data. In contrast,
likelihood-based training encourages models to cover the full support of the
train data, but yields poorer samples. These mutual shortcomings can in
principle be addressed by training generative latent variable models in a
hybrid adversarial-likelihood manner. However, we show that commonly made
parametric assumptions create a conflict between them, making successful hybrid
models non trivial. As a solution, we propose to use deep invertible
transformations in the latent variable decoder. This approach allows for
likelihood computations in image space, is more efficient than fully invertible
models, and can take full advantage of adversarial training. We show that our
model significantly improves over existing hybrid models: offering GAN-like
samples, IS and FID scores that are competitive with fully adversarial models,
and improved likelihood scores
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
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
- …