Mohammad Norouzi

research

∙ 06/14/2023

TryOnDiffusion: A Tale of Two UNets

Given two images depicting a person and a garment worn by another person...

2 Luyang Zhu, et al. ∙

research

∙ 06/02/2023

The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation

Denoising diffusion probabilistic models have transformed image generati...

0 Saurabh Saxena, et al. ∙

research

∙ 02/28/2023

Monocular Depth Estimation using Diffusion Models

We formulate monocular depth estimation using denoising diffusion models...

0 Saurabh Saxena, et al. ∙

research

∙ 12/20/2022

Character-Aware Models Improve Visual Text Rendering

Current image generation models struggle to reliably produce well-formed...

0 Rosanne Liu, et al. ∙

research

∙ 12/05/2022

Meta-Learning Fast Weight Language Models

Dynamic evaluation of language models (LMs) adapts model parameters at t...

0 Kevin Clark, et al. ∙

research

∙ 10/06/2022

Novel View Synthesis with Diffusion Models

We present 3DiM, a diffusion model for 3D novel view synthesis, which is...

0 Daniel Watson, 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

∙ 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

∙ 05/23/2022

Decoder Denoising Pretraining for Semantic Segmentation

Semantic segmentation labels are expensive and time consuming to acquire...

4 Emmanuel Brempong Asiedu, et al. ∙

research

∙ 05/19/2022

Robust and Efficient Medical Imaging with Self-Supervision

Recent progress in Medical Artificial Intelligence (AI) has delivered sy...

24 Shekoofeh Azizi, 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

∙ 02/11/2022

Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality

Diffusion models have emerged as an expressive family of generative mode...

6 Daniel Watson, 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

∙ 06/17/2021

WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

This paper introduces WaveGrad 2, a non-autoregressive generative model ...

0 Nanxin Chen, et al. ∙

research

∙ 06/11/2021

Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation

Semi-Supervised Learning (SSL) has seen success in many application doma...

0 Xuanli He, et al. ∙

research

∙ 06/07/2021

Learning to Efficiently Sample from Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a power...

0 Daniel Watson, et al. ∙

research

∙ 04/28/2021

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Standard dynamics models for continuous control make use of feedforward ...

5 Michael R. Zhang, 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

∙ 04/05/2021

SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network

We present SpeechStew, a speech recognition model that is trained on a c...

0 William Chan, et al. ∙

research

∙ 03/30/2021

Benchmarks for Deep Off-Policy Evaluation

Off-policy evaluation (OPE) holds the promise of being able to leverage ...

13 Justin Fu, et al. ∙

research

∙ 01/13/2021

Big Self-Supervised Models Advance Medical Image Classification

Self-supervised pretraining followed by supervised fine-tuning has seen ...

22 Shekoofeh Azizi, et al. ∙

research

∙ 10/30/2020

What's in a Loss Function for Image Classification?

It is common to use the softmax cross-entropy loss to train neural netwo...

31 Simon Kornblith, et al. ∙

research

∙ 10/08/2020

No MCMC for me: Amortized sampling for fast and stable training of energy-based models

Energy-Based Models (EBMs) present a flexible and appealing way to repre...

6 Will Grathwohl, et al. ∙

research

∙ 10/05/2020

Mastering Atari with Discrete World Models

Intelligent agents need to generalize from past experience to achieve go...

17 Danijar Hafner, et al. ∙

research

∙ 09/02/2020

WaveGrad: Estimating Gradients for Waveform Generation

This paper introduces WaveGrad, a conditional model for waveform generat...

5 Nanxin Chen, et al. ∙

research

∙ 06/24/2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...

10 Caglar Gulcehre, et al. ∙

research

∙ 06/17/2020

Big Self-Supervised Models are Strong Semi-Supervised Learners

One paradigm for learning from few labeled examples while making best us...

6 Ting Chen, et al. ∙

research

∙ 05/03/2020

Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation

This paper introduces Dynamic Programming Encoding (DPE), a new segmenta...

0 Xuanli He, et al. ∙

research

∙ 04/16/2020

Non-Autoregressive Machine Translation with Latent Alignments

This paper investigates two latent alignment models for non-autoregressi...

0 Chitwan Saharia, et al. ∙

research

∙ 04/09/2020

Exemplar VAEs for Exemplar based Generation and Data Augmentation

This paper presents a framework for exemplar based generative modeling, ...

11 Sajad Norouzi, et al. ∙

research

∙ 04/06/2020

NiLBS: Neural Inverse Linear Blend Skinning

In this technical report, we investigate efficient representations of ar...

0 Timothy Jeruzalski, et al. ∙

research

∙ 04/01/2020

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Standard variational lower bounds used to train latent variable models p...

8 Yucen Luo, et al. ∙

research

∙ 02/20/2020

Imputer: Sequence Modelling via Imputation and Dynamic Programming

This paper presents the Imputer, a neural sequence model that generates ...

0 William Chan, et al. ∙

research

∙ 02/13/2020

A Simple Framework for Contrastive Learning of Visual Representations

This paper presents SimCLR: a simple framework for contrastive learning ...

13 Ting Chen, et al. ∙

research

∙ 12/06/2019

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

We propose to reinterpret a standard discriminative classifier of p(y|x)...

19 Will Grathwohl, et al. ∙

research

∙ 12/06/2019

NASA: Neural Articulated Shape Approximation

Efficient representation of articulated objects such as human bodies is ...

8 Timothy Jeruzalski, et al. ∙

research

∙ 12/03/2019

Dream to Control: Learning Behaviors by Latent Imagination

Learned world models summarize an agent's experience to facilitate learn...

0 Danijar Hafner, et al. ∙

research

∙ 11/06/2019

Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse

Posterior collapse in Variational Autoencoders (VAEs) arises when the va...

33 James Lucas, et al. ∙

research

∙ 07/24/2019

Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy

This paper proposes a method for learning a trajectory-conditioned polic...

1 Yijie Guo, et al. ∙

research

∙ 07/10/2019

Striving for Simplicity in Off-policy Deep Reinforcement Learning

Reflecting on the advances of off-policy deep reinforcement learning (RL...

2 Rishabh Agarwal, et al. ∙

research

∙ 05/01/2019

Similarity of Neural Network Representations Revisited

Recent work has sought to understand the behavior of neural networks by ...

20 Simon Kornblith, et al. ∙

research

∙ 02/19/2019

Learning to Generalize from Sparse and Underspecified Rewards

We consider the problem of learning from sparse and underspecified rewar...

0 Rishabh Agarwal, et al. ∙

research

∙ 11/27/2018

Understanding the impact of entropy on policy optimization

Entropy regularization is commonly used to improve policy optimization i...

0 Zafarali Ahmed, et al. ∙

research

∙ 11/27/2018

Understanding the impact of entropy in policy learning

Entropy regularization is commonly used to improve policy optimization i...

0 Zafarali Ahmed, et al. ∙

research

∙ 11/05/2018

Contingency-Aware Exploration in Reinforcement Learning

This paper investigates whether learning contingency-awareness and contr...

8 Jongwook Choi, et al. ∙

research

∙ 10/17/2018

Sequence to Sequence Mixture Model for Diverse Machine Translation

Sequence to sequence (SEQ2SEQ) models often lack diversity in their gene...

0 Xuanli He, et al. ∙

research

∙ 10/02/2018

Optimal Completion Distillation for Sequence Learning

We present Optimal Completion Distillation (OCD), a training procedure f...

6 Sara Sabour, et al. ∙

research

∙ 08/23/2018

The Importance of Generation Order in Language Modeling

Neural language models are a critical component of state-of-the-art syst...

0 Nicolas Ford, et al. ∙

research

∙ 07/06/2018

Memory Augmented Policy Optimization for Program Synthesis with Generalization

This paper presents Memory Augmented Policy Optimization (MAPO): a novel...

6 Chen Liang, et al. ∙

research

∙ 07/05/2018

Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

This paper presents KeypointNet, an end-to-end geometric reasoning frame...

0 Supasorn Suwajanakorn, et al. ∙

Mohammad Norouzi

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