activity
20182022
most citedLingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

184 citations · 186 across the 2 of their papers we have counts for

collaborators

5 papers

cs.PL20222 cited

TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment

Hsin-I Cindy Liu, Marius Brehler, Mahesh Ravishankar +3

Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focus…

cs.PL2022

Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction

Nicolas Vasilache, Oleksandr Zinenko, Aart J. C. Bik +9

Despite significant investment in software infrastructure, machine learning systems, runtimes and compilers do not compose properly. We propose a new design aiming at providing unp…

eess.AS2020

Streaming keyword spotting on mobile devices

Oleg Rybakov, Natasha Kononenko, Niranjan Subrahmanya +2

In this work we explore the latency and accuracy of keyword spotting (KWS) models in streaming and non-streaming modes on mobile phones. NN model conversion from non-streaming mode…

cs.LG2019184 cited

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…

cs.CL2018

Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation

Ye Jia, Melvin Johnson, Wolfgang Macherey +6

End-to-end Speech Translation (ST) models have many potential advantages when compared to the cascade of Automatic Speech Recognition (ASR) and text Machine Translation (MT) models…