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06/14/2023
TryOnDiffusion: A Tale of Two UNets
Given two images depicting a person and a garment worn by another person...
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06/02/2023
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Denoising diffusion probabilistic models have transformed image generati...
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02/28/2023
Monocular Depth Estimation using Diffusion Models
We formulate monocular depth estimation using denoising diffusion models...
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12/20/2022
Character-Aware Models Improve Visual Text Rendering
Current image generation models struggle to reliably produce well-formed...
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12/05/2022
Meta-Learning Fast Weight Language Models
Dynamic evaluation of language models (LMs) adapts model parameters at t...
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10/06/2022
Novel View Synthesis with Diffusion Models
We present 3DiM, a diffusion model for 3D novel view synthesis, which is...
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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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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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05/23/2022
Decoder Denoising Pretraining for Semantic Segmentation
Semantic segmentation labels are expensive and time consuming to acquire...
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05/19/2022
Robust and Efficient Medical Imaging with Self-Supervision
Recent progress in Medical Artificial Intelligence (AI) has delivered sy...
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04/07/2022
Video Diffusion Models
Generating temporally coherent high fidelity video is an important miles...
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02/11/2022
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
Diffusion models have emerged as an expressive family of generative mode...
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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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06/17/2021
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
This paper introduces WaveGrad 2, a non-autoregressive generative model ...
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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...
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06/07/2021
Learning to Efficiently Sample from Diffusion Probabilistic Models
Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a power...
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04/28/2021
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Standard dynamics models for continuous control make use of feedforward ...
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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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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...
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03/30/2021
Benchmarks for Deep Off-Policy Evaluation
Off-policy evaluation (OPE) holds the promise of being able to leverage ...
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01/13/2021
Big Self-Supervised Models Advance Medical Image Classification
Self-supervised pretraining followed by supervised fine-tuning has seen ...
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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...
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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...
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10/05/2020
Mastering Atari with Discrete World Models
Intelligent agents need to generalize from past experience to achieve go...
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09/02/2020
WaveGrad: Estimating Gradients for Waveform Generation
This paper introduces WaveGrad, a conditional model for waveform generat...
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06/24/2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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06/17/2020
Big Self-Supervised Models are Strong Semi-Supervised Learners
One paradigm for learning from few labeled examples while making best us...
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05/03/2020
Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation
This paper introduces Dynamic Programming Encoding (DPE), a new segmenta...
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04/16/2020
Non-Autoregressive Machine Translation with Latent Alignments
This paper investigates two latent alignment models for non-autoregressi...
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04/09/2020
Exemplar VAEs for Exemplar based Generation and Data Augmentation
This paper presents a framework for exemplar based generative modeling, ...
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04/06/2020
NiLBS: Neural Inverse Linear Blend Skinning
In this technical report, we investigate efficient representations of ar...
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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...
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02/20/2020
Imputer: Sequence Modelling via Imputation and Dynamic Programming
This paper presents the Imputer, a neural sequence model that generates ...
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02/13/2020
A Simple Framework for Contrastive Learning of Visual Representations
This paper presents SimCLR: a simple framework for contrastive learning ...
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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)...
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12/06/2019
NASA: Neural Articulated Shape Approximation
Efficient representation of articulated objects such as human bodies is ...
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12/03/2019
Dream to Control: Learning Behaviors by Latent Imagination
Learned world models summarize an agent's experience to facilitate learn...
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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...
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07/24/2019
Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy
This paper proposes a method for learning a trajectory-conditioned polic...
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07/10/2019
Striving for Simplicity in Off-policy Deep Reinforcement Learning
Reflecting on the advances of off-policy deep reinforcement learning (RL...
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05/01/2019
Similarity of Neural Network Representations Revisited
Recent work has sought to understand the behavior of neural networks by ...
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02/19/2019
Learning to Generalize from Sparse and Underspecified Rewards
We consider the problem of learning from sparse and underspecified rewar...
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11/27/2018
Understanding the impact of entropy on policy optimization
Entropy regularization is commonly used to improve policy optimization i...
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11/27/2018
Understanding the impact of entropy in policy learning
Entropy regularization is commonly used to improve policy optimization i...
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11/05/2018
Contingency-Aware Exploration in Reinforcement Learning
This paper investigates whether learning contingency-awareness and contr...
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10/17/2018
Sequence to Sequence Mixture Model for Diverse Machine Translation
Sequence to sequence (SEQ2SEQ) models often lack diversity in their gene...
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10/02/2018
Optimal Completion Distillation for Sequence Learning
We present Optimal Completion Distillation (OCD), a training procedure f...
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08/23/2018
The Importance of Generation Order in Language Modeling
Neural language models are a critical component of state-of-the-art syst...
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07/06/2018
Memory Augmented Policy Optimization for Program Synthesis with Generalization
This paper presents Memory Augmented Policy Optimization (MAPO): a novel...
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07/05/2018
