activity
20152026
most citedDeep Speech 2: End-to-End Speech Recognition in English and Mandarin

2.2k citations · 5.4k across the 32 of their papers we have counts for

collaborators
Showing 2020Show all

7 papers · 1 filter

eess.AS2020★ 354 cited

MLS: A Large-Scale Multilingual Dataset for Speech Research

Vineel Pratap, Qiantong Xu, Anuroop Sriram +2

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox an…

eess.IV2020★ 313 cited

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh +20

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reco…

cond-mat.mtrl-sci2020★ 20 cited

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das +14

Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reli…

cond-mat.mtrl-sci2020★ 793 cited

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

Lowik Chanussot, Abhishek Das, Siddharth Goyal +14

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer produc…

eess.AS2020

Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters

Vineel Pratap, Anuroop Sriram, Paden Tomasello +4

We study training a single acoustic model for multiple languages with the aim of improving automatic speech recognition (ASR) performance on low-resource languages, and over-all si…

eess.IV2020

End-to-End Variational Networks for Accelerated MRI Reconstruction

Anuroop Sriram, Jure Zbontar, Tullie Murrell +5

The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (para…

eess.IV2020★ 267 cited

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

Florian Knoll, Tullie Murrell, Anuroop Sriram +8

Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space da…