Anuroop Sriram

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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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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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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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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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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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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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Anuroop Sriram

Featured Co-authors

Yi Li

Pascal Vincent

Gabriel Synnaeve

Edouard Grave

Ronan Collobert

C. Lawrence Zitnick

Aaron Defazio

Zizhao Zhang

Michael Rabbat

Michal Drozdzal

Adriana Romero

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

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

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

End-to-End Variational Networks for Accelerated MRI Reconstruction

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

RNN-T For Latency Controlled ASR With Improved Beam Search

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