Search Results for author: Anuroop Sriram

Found 33 papers, 19 papers with code

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

no code implementations16 Apr 2025 Aaron Havens, Benjamin Kurt Miller, Bing Yan, Carles Domingo-Enrich, Anuroop Sriram, Brandon Wood, Daniel Levine, Bin Hu, Brandon Amos, Brian Karrer, Xiang Fu, Guan-Horng Liu, Ricky T. Q. Chen

We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions.

Computational chemistry

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials

1 code implementation5 Mar 2025 Chaitanya K. Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, Zachary W. Ulissi

We introduce the All-atom Diffusion Transformer (ADiT), a unified latent diffusion framework for jointly generating both periodic materials and non-periodic molecular systems using the same model: (1) An autoencoder maps a unified, all-atom representations of molecules and materials to a shared latent embedding space; and (2) A diffusion model is trained to generate new latent embeddings that the autoencoder can decode to sample new molecules or materials.

All Unconditional Crystal Generation +1

Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective

no code implementations4 Dec 2024 Neta Shaul, Itai Gat, Marton Havasi, Daniel Severo, Anuroop Sriram, Peter Holderrieth, Brian Karrer, Yaron Lipman, Ricky T. Q. Chen

Through the lens of optimizing the symmetric kinetic energy, we propose velocity formulas that can be applied to any given probability path, completely decoupling the probability and velocity, and giving the user the freedom to specify any desirable probability path based on expert knowledge specific to the data domain.

Image Generation Text Generation

FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions

1 code implementation30 Oct 2024 Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, Brandon M. Wood

Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics.

Unconditional Crystal Generation

Distribution Learning for Molecular Regression

no code implementations30 Jul 2024 Nima Shoghi, Pooya Shoghi, Anuroop Sriram, Abhishek Das

Using "soft" targets to improve model performance has been shown to be effective in classification settings, but the usage of soft targets for regression is a much less studied topic in machine learning.

Mixture-of-Experts Molecular Property Prediction +2

FlowMM: Generating Materials with Riemannian Flow Matching

1 code implementation7 Jun 2024 Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M Wood

Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges.

Unconditional Crystal Generation

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

no code implementations27 Jun 2022 Anuroop Sriram, Michael Auli, Alexei Baevski

Self-supervised learning (SSL) of speech representations has received much attention over the last few years but most work has focused on languages and domains with an abundance of unlabeled data.

Data Augmentation Self-Supervised Learning

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

1 code implementation ICLR 2022 Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, C. Lawrence Zitnick

Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change.

Initial Structure to Relaxed Energy (IS2RE)

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

3 code implementations2 Apr 2021 Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli

On a large-scale competitive setup, we show that pre-training on unlabeled in-domain data reduces the gap between models trained on in-domain and out-of-domain labeled data by 66%-73%.

Self-Supervised Learning

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

1 code implementation13 Jan 2021 Anuroop Sriram, Matthew Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, William Moore

The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0. 742 for predicting an adverse event within 96 hours (compared to 0. 703 with supervised pretraining) and an AUC of 0. 765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0. 749 with supervised pretraining).

Prognosis Representation Learning +1

ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation

no code implementations1 Jan 2021 Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, Larry Zitnick

We use ForceNet to perform quantum chemistry simulations, where ForceNet is able to achieve 4x higher success rate than existing ML models.

Atomic Forces Graph Neural Network

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

no code implementations6 Jul 2020 Vineel Pratap, Anuroop Sriram, Paden Tomasello, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

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 simplifying deployment of ASR systems that support diverse languages.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

End-to-End Variational Networks for Accelerated MRI Reconstruction

3 code implementations14 Apr 2020 Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia Johnson

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 (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).

Anatomy compressed sensing +1

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

1 code implementation6 Jan 2020 Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht

Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

BIG-bench Machine Learning Image Reconstruction

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

1 code implementation19 Nov 2019 Gabriel Synnaeve, Qiantong Xu, Jacob Kahn, Tatiana Likhomanenko, Edouard Grave, Vineel Pratap, Anuroop Sriram, Vitaliy Liptchinsky, Ronan Collobert

We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq loss functions.

Ranked #19 on Speech Recognition on LibriSpeech test-other (using extra training data)

Language Modeling Language Modelling +2

RNN-T For Latency Controlled ASR With Improved Beam Search

no code implementations5 Nov 2019 Mahaveer Jain, Kjell Schubert, Jay Mahadeokar, Ching-Feng Yeh, Kaustubh Kalgaonkar, Anuroop Sriram, Christian Fuegen, Michael L. Seltzer

Neural transducer-based systems such as RNN Transducers (RNN-T) for automatic speech recognition (ASR) blend the individual components of a traditional hybrid ASR systems (acoustic model, language model, punctuation model, inverse text normalization) into one single model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

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

1 code implementation CVPR 2020 Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson

In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors.

compressed sensing MRI Reconstruction

Robust Speech Recognition Using Generative Adversarial Networks

no code implementations5 Nov 2017 Anuroop Sriram, Heewoo Jun, Yashesh Gaur, Sanjeev Satheesh

This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition.

Generative Adversarial Network Robust Speech Recognition +1

Cold Fusion: Training Seq2Seq Models Together with Language Models

no code implementations ICLR 2018 Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, Adam Coates

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition.

Image Captioning Language Modeling +5

Exploring Neural Transducers for End-to-End Speech Recognition

no code implementations24 Jul 2017 Eric Battenberg, Jitong Chen, Rewon Child, Adam Coates, Yashesh Gaur, Yi Li, Hairong Liu, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu

In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition.

Language Modeling Language Modelling +2

Reducing Bias in Production Speech Models

no code implementations11 May 2017 Eric Battenberg, Rewon Child, Adam Coates, Christopher Fougner, Yashesh Gaur, Jiaji Huang, Heewoo Jun, Ajay Kannan, Markus Kliegl, Atul Kumar, Hairong Liu, Vinay Rao, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu

Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition.

Object Recognition

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