7.3k citations · 21.3k across the 74 of their papers we have counts for
10 papers · 1 filter
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
Nanxin Chen, Yu Zhang, Heiga Zen +4
This paper introduces WaveGrad 2, a non-autoregressive generative model for text-to-speech synthesis. WaveGrad 2 is trained to estimate the gradient of the log conditional density…
Learning to Efficiently Sample from Diffusion Probabilistic Models
Daniel Watson, Jonathan Ho, Mohammad Norouzi +1
Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a powerful family of generative models that can yield high-fidelity samples and competitive log-likelihoods across…
Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan +3
We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary…
Generate, Annotate, and Learn: NLP with Synthetic Text
Xuanli He, Islam Nassar, Jamie Kiros +2
This paper studies the use of language models as a source of synthetic unlabeled text for NLP. We formulate a general framework called ``generate, annotate, and learn (GAL)'' to ta…
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Michael R. Zhang, Tom Le Paine, Ofir Nachum +4
Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action…
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
William Chan, Daniel Park, Chris Lee +3
We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, Lib…