554 citations · 764 across the 5 of their papers we have counts for
11 papers
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
Chung-Cheng Chiu, Arun Narayanan, Wei Han +8
In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing wor…
Robotic Table Tennis with Model-Free Reinforcement Learning
Wenbo Gao, Laura Graesser, Krzysztof Choromanski +5
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolut…
Imputer: Sequence Modelling via Imputation and Dynamic Programming
William Chan, Chitwan Saharia, Geoffrey Hinton +2
This paper presents the Imputer, a neural sequence model that generates output sequences iteratively via imputations. The Imputer is an iterative generative model, requiring only a…
SPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping Pong
Steven Schwarcz, Peng Xu, David D'Ambrosio +4
We introduce a new high resolution, high frame rate stereo video dataset, which we call SPIN, for tracking and action recognition in the game of ping pong. The corpus consists of p…
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…
Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play
Reza Mahjourian, Risto Miikkulainen, Nevena Lazic +2
Training robots with physical bodies requires developing new methods and action representations that allow the learning agents to explore the space of policies efficiently. This wo…