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01/26/2023
simple diffusion: End-to-end diffusion for high resolution images
Currently, applying diffusion models in pixel space of high resolution i...
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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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09/12/2022
Blurring Diffusion Models
Recently, Rissanen et al., (2022) have presented a new type of diffusion...
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07/26/2022
Classifier-Free Diffusion Guidance
Classifier guidance is a recently introduced method to trade off mode co...
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06/17/2022
Lossy Compression with Gaussian Diffusion
We describe a novel lossy compression approach called DiffC which is bas...
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05/23/2022
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented...
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04/07/2022
Video Diffusion Models
Generating temporally coherent high fidelity video is an important miles...
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02/01/2022
Progressive Distillation for Fast Sampling of Diffusion Models
Diffusion models have recently shown great promise for generative modeli...
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11/10/2021
Palette: Image-to-Image Diffusion Models
We introduce Palette, a simple and general framework for image-to-image ...
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10/05/2021
Autoregressive Diffusion Models
We introduce Autoregressive Diffusion Models (ARDMs), a model class enco...
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07/01/2021
Variational Diffusion Models
Diffusion-based generative models have demonstrated a capacity for perce...
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04/19/2021
Agent-Centric Representations for Multi-Agent Reinforcement Learning
Object-centric representations have recently enabled significant progres...
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04/15/2021
Image Super-Resolution via Iterative Refinement
We present SR3, an approach to image Super-Resolution via Repeated Refin...
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08/03/2020
A Spectral Energy Distance for Parallel Speech Synthesis
Speech synthesis is an important practical generative modeling problem t...
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06/22/2020
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
In this paper we analyse and improve integer discrete flows for lossless...
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03/26/2020
Milking CowMask for Semi-Supervised Image Classification
Consistency regularization is a technique for semi-supervised learning t...
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03/24/2020
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct ...
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02/07/2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Variational Bayesian Inference is a popular methodology for approximatin...
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02/06/2020
How Good is the Bayes Posterior in Deep Neural Networks Really?
During the past five years the Bayesian deep learning community has deve...
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01/14/2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Ensembles of models have been empirically shown to improve predictive pe...
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12/20/2019
Axial Attention in Multidimensional Transformers
We propose Axial Transformers, a self-attention-based autoregressive mod...
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12/13/2019
Dota 2 with Large Scale Deep Reinforcement Learning
On April 13th, 2019, OpenAI Five became the first AI system to defeat th...
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04/07/2019
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based searc...
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12/08/2018
Learning Montezuma's Revenge from a Single Demonstration
We propose a new method for learning from a single demonstration to solv...
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03/15/2018
Improving GANs Using Optimal Transport
We present Optimal Transport GAN (OT-GAN), a variant of generative adver...
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03/10/2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
We explore the use of Evolution Strategies (ES), a class of black box op...
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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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06/10/2016
Improved Techniques for Training GANs
We present a variety of new architectural features and training procedur...
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02/28/2016
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features
In this note we present a generative model of natural images consisting ...
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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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06/28/2012
