Anuroop Sriram

research

∙ 06/29/2022

Spherical Channels for Modeling Atomic Interactions

Modeling the energy and forces of atomic systems is a fundamental proble...

C. Lawrence Zitnick, et al. ∙

research

∙ 06/27/2022

Wav2Vec-Aug: Improved self-supervised training with limited data

Self-supervised learning (SSL) of speech representations has received mu...

Anuroop Sriram, et al. ∙

research

∙ 06/17/2022

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis

Computational catalysis and machine learning communities have made consi...

Richard Tran, et al. ∙

research

∙ 04/06/2022

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

The predominant method of demonstrating progress of atomic graph neural ...

Johannes Gasteiger, et al. ∙

research

∙ 03/18/2022

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

Recent progress in Graph Neural Networks (GNNs) for modeling atomic simu...

Anuroop Sriram, et al. ∙

research

∙ 06/17/2021

Rotation Invariant Graph Neural Networks using Spin Convolutions

Progress towards the energy breakthroughs needed to combat climate chang...

Muhammed Shuaibi, et al. ∙

research

∙ 04/02/2021

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

Self-supervised learning of speech representations has been a very activ...

Wei-Ning Hsu, et al. ∙

research

∙ 03/02/2021

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

With massive amounts of atomic simulation data available, there is a hug...

Weihua Hu, et al. ∙

research

∙ 01/13/2021

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...

Anuroop Sriram, et al. ∙

research

∙ 12/09/2020

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...

Matthew J. Muckley, et al. ∙

research

∙ 12/07/2020

MLS: A Large-Scale Multilingual Dataset for Speech Research

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large mu...

Vineel Pratap, et al. ∙

research

∙ 10/20/2020

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

Catalyst discovery and optimization is key to solving many societal and ...

Lowik Chanussot, et al. ∙

research

∙ 10/14/2020

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

Scalable and cost-effective solutions to renewable energy storage are es...

C. Lawrence Zitnick, et al. ∙

research

∙ 07/06/2020

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

We study training a single acoustic model for multiple languages with th...

Vineel Pratap, et al. ∙

research

∙ 04/14/2020

End-to-End Variational Networks for Accelerated MRI Reconstruction

The slow acquisition speed of magnetic resonance imaging (MRI) has led t...

Anuroop Sriram, et al. ∙

research

∙ 01/06/2020

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...

Florian Knoll, et al. ∙

research

∙ 11/19/2019

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

We study ResNet-, Time-Depth Separable ConvNets-, and Transformer-based ...

Gabriel Synnaeve, et al. ∙

research

∙ 11/05/2019

RNN-T For Latency Controlled ASR With Improved Beam Search

Neural transducer-based systems such as RNN Transducers (RNN-T) for auto...

Mahaveer Jain, et al. ∙

research

∙ 10/27/2019

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

Magnetic Resonance Image (MRI) acquisition is an inherently slow process...

Anuroop Sriram, et al. ∙

research

∙ 11/21/2018

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...

Jure Žbontar, et al. ∙

research

∙ 11/05/2017

Robust Speech Recognition Using Generative Adversarial Networks

This paper describes a general, scalable, end-to-end framework that uses...

Anuroop Sriram, et al. ∙

research

∙ 08/21/2017

Cold Fusion: Training Seq2Seq Models Together with Language Models

Sequence-to-sequence (Seq2Seq) models with attention have excelled at ta...

Anuroop Sriram, et al. ∙

research

∙ 07/24/2017

Exploring Neural Transducers for End-to-End Speech Recognition

In this work, we perform an empirical comparison among the CTC, RNN-Tran...

Eric Battenberg, et al. ∙

research

∙ 05/11/2017

Reducing Bias in Production Speech Models

Replacing hand-engineered pipelines with end-to-end deep learning system...

Eric Battenberg, et al. ∙

Anuroop Sriram

Featured Co-authors

Jure Leskovec

Joelle Pineau

Yi Li

Devi Parikh

Stephan Günnemann

Michael Auli

Gabriel Synnaeve

Pascal Vincent

Edouard Grave

Wei-Ning Hsu

Ronan Collobert

Spherical Channels for Modeling Atomic Interactions

Wav2Vec-Aug: Improved self-supervised training with limited data

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysis

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

Rotation Invariant Graph Neural Networks using Spin Convolutions

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

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

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

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

MLS: A Large-Scale Multilingual Dataset for Speech Research

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

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

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