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VideoFlow: A Flow-Based Generative Model for Video
Generative models that can model and predict sequences of future events ...
03/04/2019 ∙ by Manoj Kumar, et al. ∙
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Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling hig...
05/24/2019 ∙ by Dustin Tran, et al. ∙
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A RAD approach to deep mixture models
Flow based models such as Real NVP are an extremely powerful approach to...
03/18/2019 ∙ by Laurent Dinh, et al. ∙
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Sharp Minima Can Generalize For Deep Nets
Despite their overwhelming capacity to overfit, deep learning architectu...
03/15/2017 ∙ by Laurent Dinh, et al. ∙
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Learnable Explicit Density for Continuous Latent Space and Variational Inference
In this paper, we study two aspects of the variational autoencoder (VAE)...
10/06/2017 ∙ by Chin-Wei Huang, et al. ∙
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Techniques for Learning Binary Stochastic Feedforward Neural Networks
Stochastic binary hidden units in a multi-layer perceptron (MLP) network...
06/11/2014 ∙ by Tapani Raiko, et al. ∙
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Density estimation using Real NVP
Unsupervised learning of probabilistic models is a central yet challengi...
05/27/2016 ∙ by Laurent Dinh, et al. ∙
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Predicting Parameters in Deep Learning
We demonstrate that there is significant redundancy in the parameterizat...
06/03/2013 ∙ by Misha Denil, et al. ∙
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Learning Awareness Models
We consider the setting of an agent with a fixed body interacting with a...
04/17/2018 ∙ by Brandon Amos, et al. ∙
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Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
05/09/2016 ∙ by The Theano Development Team, et al. ∙
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