no code implementations • NeurIPS 2009 • Tom Ouyang, Randall Davis
We propose a new sketch recognition framework that combines a rich representation of low level visual appearance with a graphical model for capturing high level relationships between symbols.
no code implementations • 13 Apr 2017 • Tom Ouyang, David Rybach, Françoise Beaufays, Michael Riley
We describe the general framework of what we call for short the keyboard "FST decoder" as well as the implementation details that are new compared to a speech FST decoder.
no code implementations • 26 Mar 2019 • Mingqing Chen, Rajiv Mathews, Tom Ouyang, Françoise Beaufays
We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vocabulary of a virtual keyboard for smartphones without exporting sensitive text to servers.
no code implementations • 15 Oct 2019 • Yin Zhou, Pei Sun, Yu Zhang, Dragomir Anguelov, Jiyang Gao, Tom Ouyang, James Guo, Jiquan Ngiam, Vijay Vasudevan
In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both.
no code implementations • 17 Jan 2022 • Andreas Kabel, Keith Hall, Tom Ouyang, David Rybach, Daan van Esch, Françoise Beaufays
This paper proposes a framework to improve the typing experience of mobile users in morphologically rich languages.