2.1k citations · 3.1k across the 18 of their papers we have counts for
7 papers · 1 filter
Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
Daniel Watson, William Chan, Jonathan Ho +1
Diffusion models have emerged as an expressive family of generative models rivaling GANs in sample quality and autoregressive models in likelihood scores. Standard diffusion models…
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…
Insertion-Deletion Transformer
Laura Ruis, Mitchell Stern, Julia Proskurnia +1
We propose the Insertion-Deletion Transformer, a novel transformer-based neural architecture and training method for sequence generation. The model consists of two phases that are…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
Optimal Completion Distillation for Sequence Learning
Sara Sabour, William Chan, Mohammad Norouzi
We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters…
Transferring Knowledge from a RNN to a DNN
William Chan, Nan Rosemary Ke, Ian Lane
Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent Neural Network (RNN) mode…