Anuroop Sriram

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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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Anuroop Sriram

Featured Co-authors

Yi Li

Pascal Vincent

Gabriel Synnaeve

Edouard Grave

Ronan Collobert

C. Lawrence Zitnick

Aaron Defazio

Zizhao Zhang

Michael Rabbat

Awni Hannun

Michal Drozdzal

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

End-to-End Variational Networks for Accelerated MRI Reconstruction

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

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

RNN-T For Latency Controlled ASR With Improved Beam Search

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

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Robust Speech Recognition Using Generative Adversarial Networks

Cold Fusion: Training Seq2Seq Models Together with Language Models

Exploring Neural Transducers for End-to-End Speech Recognition

Reducing Bias in Production Speech Models