no code implementations • 16 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.
1 code implementation • 5 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.
Ranked #1 on
Unconditional Molecule Generation
on QM9
no code implementations • 4 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.
1 code implementation • 30 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.
Ranked #2 on
Unconditional Crystal Generation
on MP20
no code implementations • 30 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.
1 code implementation • 7 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.
Ranked #3 on
Unconditional Crystal Generation
on MP20
3 code implementations • 6 Feb 2024 • Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ulissi
We propose fine-tuning large language models for generation of stable materials.
1 code implementation • 1 Nov 2023 • Anuroop Sriram, Sihoon Choi, Xiaohan Yu, Logan M. Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J. Medford, David S. Sholl
We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level.
2 code implementations • 29 Jun 2022 • C. Lawrence Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, Brandon Wood
We propose the Spherical Channel Network (SCN) to model atomic energies and forces.
no code implementations • 27 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.
1 code implementation • 17 Jun 2022 • Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Felix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, C. Lawrence Zitnick
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.
no code implementations • 6 Apr 2022 • Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C. Lawrence Zitnick, Abhishek Das
This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset.
Ranked #1 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
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.
Ranked #2 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
1 code implementation • 17 Jun 2021 • Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, C. Lawrence Zitnick
We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network.
Ranked #3 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
Graph Neural Network
Initial Structure to Relaxed Energy (IS2RE)
3 code implementations • 2 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%.
no code implementations • 2 Mar 2021 • Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, C. Lawrence Zitnick
By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency.
1 code implementation • 13 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).
no code implementations • 1 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.
3 code implementations • 9 Dec 2020 • Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh, Sunwoo Kim, Geunu Jeong, Jingyu Ko, Yohan Jun, Hyungseob Shin, Dosik Hwang, Mahmoud Mostapha, Simon Arberet, Dominik Nickel, Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck, Jonas Teuwen, Dimitrios Karkalousos, Chaoping Zhang, Anuroop Sriram, Zhengnan Huang, Nafissa Yakubova, Yvonne Lui, Florian Knoll
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
1 code implementation • 7 Dec 2020 • Vineel Pratap, Qiantong Xu, Anuroop Sriram, Gabriel Synnaeve, Ronan Collobert
This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
5 code implementations • 20 Oct 2020 • Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.
no code implementations • 14 Oct 2020 • C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Brandon Wood, Junwoong Yoon, Devi Parikh, Zachary Ulissi
As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand.
no code implementations • 6 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
3 code implementations • 14 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).
Ranked #1 on
MRI Reconstruction
on fastMRI Knee 4x
1 code implementation • 6 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.
1 code implementation • 19 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)
no code implementations • 5 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
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.
13 code implementations • 21 Nov 2018 • Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
no code implementations • 5 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.
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.
no code implementations • 24 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.
no code implementations • 11 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.