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03/01/2023
Understanding the Diffusion Objective as a Weighted Integral of ELBOs
Diffusion models in the literature are optimized with various objectives...
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10/06/2022
On Distillation of Guided Diffusion Models
Classifier-free guided diffusion models have recently been shown to be h...
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10/05/2022
Imagen Video: High Definition Video Generation with Diffusion Models
We present Imagen Video, a text-conditional video generation system base...
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07/01/2021
Variational Diffusion Models
Diffusion-based generative models have demonstrated a capacity for perce...
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11/06/2020
Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis
We describe a sequence-to-sequence neural network which can directly gen...
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07/01/2020
On Linear Identifiability of Learned Representations
Identifiability is a desirable property of a statistical model: it impli...
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02/26/2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models
Despite the growing popularity of energy-based models, their identifiabi...
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12/02/2019
Flow Contrastive Estimation of Energy-Based Models
This paper studies a training method to jointly estimate an energy-based...
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07/10/2019
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
The framework of variational autoencoders allows us to efficiently learn...
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06/06/2019
An Introduction to Variational Autoencoders
Variational autoencoders provide a principled framework for learning dee...
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07/09/2018
Glow: Generative Flow with Invertible 1x1 Convolutions
Flow-based generative models (Dinh et al., 2014) are conceptually attrac...
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12/04/2017
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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01/19/2017
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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11/08/2016
Variational Lossy Autoencoder
Representation learning seeks to expose certain aspects of observed data...
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06/15/2016
Improving Variational Inference with Inverse Autoregressive Flow
The framework of normalizing flows provides a general strategy for flexi...
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02/25/2016
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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06/08/2015
Variational Dropout and the Local Reparameterization Trick
We investigate a local reparameterizaton technique for greatly reducing ...
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10/23/2014
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Recent advances in stochastic gradient variational inference have made i...
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02/03/2014
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets
Hierarchical Bayesian networks and neural networks with stochastic hidde...
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12/20/2013
Auto-Encoding Variational Bayes
How can we perform efficient inference and learning in directed probabil...
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06/04/2013