Anuroop Sriram

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Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

Self-supervised learning of speech representations has been a very activ...
04/02/2021 ∙ by Wei-Ning Hsu, et al. ∙ 0 ∙ share

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ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

With massive amounts of atomic simulation data available, there is a hug...
03/02/2021 ∙ by Weihua Hu, et al. ∙ 54 ∙ share

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COVID-19 Deterioration Prediction via Self-Supervised Representation Learning and Multi-Image Prediction

The rapid spread of COVID-19 cases in recent months has strained hospita...
01/13/2021 ∙ by Anuroop Sriram, et al. ∙ 12 ∙ share

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State-of-the-art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge

Accelerating MRI scans is one of the principal outstanding problems in t...
12/09/2020 ∙ by Matthew J. Muckley, et al. ∙ 0 ∙ share

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MLS: A Large-Scale Multilingual Dataset for Speech Research

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large mu...
12/07/2020 ∙ by Vineel Pratap, et al. ∙ 0 ∙ share

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The Open Catalyst 2020 (OC20) Dataset and Community Challenges

Catalyst discovery and optimization is key to solving many societal and ...
10/20/2020 ∙ by Lowik Chanussot, et al. ∙ 38 ∙ share

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

Scalable and cost-effective solutions to renewable energy storage are es...
10/14/2020 ∙ by C. Lawrence Zitnick, et al. ∙ 17 ∙ share

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

We study training a single acoustic model for multiple languages with th...
07/06/2020 ∙ by Vineel Pratap, et al. ∙ 0 ∙ share

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End-to-End Variational Networks for Accelerated MRI Reconstruction

The slow acquisition speed of magnetic resonance imaging (MRI) has led t...
04/14/2020 ∙ by Anuroop Sriram, et al. ∙ 4 ∙ share

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

Purpose: To advance research in the field of machine learning for MR ima...
01/06/2020 ∙ by Florian Knoll, et al. ∙ 73 ∙ share

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End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures

We study ResNet-, Time-Depth Separable ConvNets-, and Transformer-based ...
11/19/2019 ∙ by Gabriel Synnaeve, et al. ∙ 0 ∙ share

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RNN-T For Latency Controlled ASR With Improved Beam Search

Neural transducer-based systems such as RNN Transducers (RNN-T) for auto...
11/05/2019 ∙ by Mahaveer Jain, et al. ∙ 0 ∙ share

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GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

Magnetic Resonance Image (MRI) acquisition is an inherently slow process...
10/27/2019 ∙ by Anuroop Sriram, et al. ∙ 23 ∙ share

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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...
11/21/2018 ∙ by Jure Žbontar, et al. ∙ 20 ∙ share

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Robust Speech Recognition Using Generative Adversarial Networks

This paper describes a general, scalable, end-to-end framework that uses...
11/05/2017 ∙ by Anuroop Sriram, et al. ∙ 0 ∙ share

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Cold Fusion: Training Seq2Seq Models Together with Language Models

Sequence-to-sequence (Seq2Seq) models with attention have excelled at ta...
08/21/2017 ∙ by Anuroop Sriram, et al. ∙ 0 ∙ share

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Exploring Neural Transducers for End-to-End Speech Recognition

In this work, we perform an empirical comparison among the CTC, RNN-Tran...
07/24/2017 ∙ by Eric Battenberg, et al. ∙ 0 ∙ share

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Reducing Bias in Production Speech Models

Replacing hand-engineered pipelines with end-to-end deep learning system...
05/11/2017 ∙ by Eric Battenberg, et al. ∙ 0 ∙ share

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