b'Tim Salimans'

research

∙ 01/26/2023

simple diffusion: End-to-end diffusion for high resolution images

Currently, applying diffusion models in pixel space of high resolution i...

0 Emiel Hoogeboom, et al. ∙

research

∙ 10/06/2022

On Distillation of Guided Diffusion Models

Classifier-free guided diffusion models have recently been shown to be h...

12 Chenlin Meng, et al. ∙

research

∙ 10/05/2022

Imagen Video: High Definition Video Generation with Diffusion Models

We present Imagen Video, a text-conditional video generation system base...

16 Jonathan Ho, et al. ∙

research

∙ 09/12/2022

Blurring Diffusion Models

Recently, Rissanen et al., (2022) have presented a new type of diffusion...

0 Emiel Hoogeboom, et al. ∙

research

∙ 07/26/2022

Classifier-Free Diffusion Guidance

Classifier guidance is a recently introduced method to trade off mode co...

28 Jonathan Ho, et al. ∙

research

∙ 06/17/2022

Lossy Compression with Gaussian Diffusion

We describe a novel lossy compression approach called DiffC which is bas...

1 Lucas Theis, et al. ∙

research

∙ 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...

0 Chitwan Saharia, et al. ∙

research

∙ 04/07/2022

Video Diffusion Models

Generating temporally coherent high fidelity video is an important miles...

0 Jonathan Ho, et al. ∙

research

∙ 02/01/2022

Progressive Distillation for Fast Sampling of Diffusion Models

Diffusion models have recently shown great promise for generative modeli...

0 Tim Salimans, et al. ∙

research

∙ 11/10/2021

Palette: Image-to-Image Diffusion Models

We introduce Palette, a simple and general framework for image-to-image ...

35 Chitwan Saharia, et al. ∙

research

∙ 10/05/2021

Autoregressive Diffusion Models

We introduce Autoregressive Diffusion Models (ARDMs), a model class enco...

3 Emiel Hoogeboom, et al. ∙

research

∙ 07/01/2021

Variational Diffusion Models

Diffusion-based generative models have demonstrated a capacity for perce...

7 Diederik P. Kingma, et al. ∙

research

∙ 04/19/2021

Agent-Centric Representations for Multi-Agent Reinforcement Learning

Object-centric representations have recently enabled significant progres...

0 Wenling Shang, et al. ∙

research

∙ 04/15/2021

Image Super-Resolution via Iterative Refinement

We present SR3, an approach to image Super-Resolution via Repeated Refin...

1 Chitwan Saharia, et al. ∙

research

∙ 08/03/2020

A Spectral Energy Distance for Parallel Speech Synthesis

Speech synthesis is an important practical generative modeling problem t...

3 Alexey A. Gritsenko, et al. ∙

research

∙ 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...

12 Rianne van den Berg, et al. ∙

research

∙ 03/26/2020

Milking CowMask for Semi-Supervised Image Classification

Consistency regularization is a technique for semi-supervised learning t...

4 Geoff French, et al. ∙

research

∙ 03/24/2020

MetNet: A Neural Weather Model for Precipitation Forecasting

Weather forecasting is a long standing scientific challenge with direct ...

0 Casper Kaae Sønderby, et al. ∙

research

∙ 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...

6 Jakub Świątkowski, et al. ∙

research

∙ 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...

27 Florian Wenzel, et al. ∙

research

∙ 01/14/2020

Hydra: Preserving Ensemble Diversity for Model Distillation

Ensembles of models have been empirically shown to improve predictive pe...

1 Linh Tran, et al. ∙

research

∙ 12/20/2019

Axial Attention in Multidimensional Transformers

We propose Axial Transformers, a self-attention-based autoregressive mod...

0 Jonathan Ho, et al. ∙

research

∙ 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...

13 OpenAI, et al. ∙

research

∙ 04/07/2019

Policy Gradient Search: Online Planning and Expert Iteration without Search Trees

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based searc...

4 Thomas Anthony, et al. ∙

research

∙ 12/08/2018

Learning Montezuma's Revenge from a Single Demonstration

We propose a new method for learning from a single demonstration to solv...

0 Tim Salimans, et al. ∙

research

∙ 03/15/2018

Improving GANs Using Optimal Transport

We present Optimal Transport GAN (OT-GAN), a variant of generative adver...

0 Tim Salimans, et al. ∙

research

∙ 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...

0 Tim Salimans, et al. ∙

research

∙ 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...

0 Tim Salimans, et al. ∙

research

∙ 11/08/2016

Variational Lossy Autoencoder

Representation learning seeks to expose certain aspects of observed data...

0 Xi Chen, et al. ∙

research

∙ 06/15/2016

Improving Variational Inference with Inverse Autoregressive Flow

The framework of normalizing flows provides a general strategy for flexi...

0 Diederik P. Kingma, et al. ∙

research

∙ 06/10/2016

Improved Techniques for Training GANs

We present a variety of new architectural features and training procedur...

0 Tim Salimans, et al. ∙

research

∙ 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 ...

0 Tim Salimans, et al. ∙

research

∙ 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...

0 Tim Salimans, et al. ∙

research

∙ 06/08/2015

Variational Dropout and the Local Reparameterization Trick

We investigate a local reparameterizaton technique for greatly reducing ...

0 Diederik P. Kingma, et al. ∙

research

∙ 10/23/2014

Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Recent advances in stochastic gradient variational inference have made i...

0 Tim Salimans, et al. ∙

research

∙ 06/28/2012

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression

We propose a general algorithm for approximating nonstandard Bayesian po...

0 Tim Salimans, et al. ∙

Tim Salimans

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