
On Linear Identifiability of Learned Representations
Identifiability is a desirable property of a statistical model: it impli...
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ICEBeeM: Identifiable Conditional EnergyBased Deep Models
Despite the growing popularity of energybased models, their identifiabi...
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Flow Contrastive Estimation of EnergyBased Models
This paper studies a training method to jointly estimate an energybased...
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Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn...
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An Introduction to Variational Autoencoders
Variational autoencoders provide a principled framework for learning dee...
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Glow: Generative Flow with Invertible 1x1 Convolutions
Flowbased generative models (Dinh et al., 2014) are conceptually attrac...
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Learning Sparse Neural Networks through L_0 Regularization
We propose a practical method for L_0 norm regularization for neural net...
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PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications
PixelCNNs are a recently proposed class of powerful generative models wi...
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Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
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Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexi...
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
We present weight normalization: a reparameterization of the weight vect...
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Variational Dropout and the Local Reparameterization Trick
We investigate a local reparameterizaton technique for greatly reducing ...
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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Recent advances in stochastic gradient variational inference have made i...
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Efficient GradientBased Inference through Transformations between Bayes Nets and Neural Nets
Hierarchical Bayesian networks and neural networks with stochastic hidde...
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AutoEncoding Variational Bayes
How can we perform efficient inference and learning in directed probabil...
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Fast GradientBased Inference with Continuous Latent Variable Models in Auxiliary Form
We propose a technique for increasing the efficiency of gradientbased i...
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