Matthew Le

According to our database, Matthew Le authored at least 18 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models.

Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali K. Thabet, Albert Pumarola, Yaron Lipman

CoRR, 2024

2023

Audiobox: Unified Audio Generation with Natural Language Prompts.

Apoorv Vyas, Bowen Shi, Matthew Le, Andros Tjandra, Yi-Chiao Wu, Baishan Guo, Jiemin Zhang, Xinyue Zhang, Robert Adkins, William Ngan, Jeff Wang, Ivan Cruz, Bapi Akula, Akinniyi Akinyemi, Brian Ellis, Rashel Moritz, Yael Yungster, Alice Rakotoarison, Liang Tan, Chris Summers, Carleigh Wood, Joshua Lane, Mary Williamson, Wei-Ning Hsu

CoRR, 2023

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale.

Matthew Le, Apoorv Vyas, Bowen Shi, Brian Karrer, Leda Sari, Rashel Moritz, Mary Williamson, Vimal Manohar, Yossi Adi, Jay Mahadeokar, Wei-Ning Hsu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On Kinetic Optimal Probability Paths for Generative Models.

Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel, Matthew Le, Yaron Lipman

Proceedings of the International Conference on Machine Learning, 2023

Flow Matching for Generative Modeling.

Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matthew Le

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Latent Discretization for Continuous-time Sequence Compression.

Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel, Karen Ullrich

CoRR, 2022

2021

Modeling Sparse Information Diffusion at Scale via Lazy Multivariate Hawkes Processes.

Maximilian Nickel, Matthew Le

Proceedings of the WWW '21: The Web Conference 2021, 2021

2020

Learning Multivariate Hawkes Processes at Scale.

Maximilian Nickel, Matthew Le

CoRR, 2020

2019

Satellite images and machine learning can identify remote communities to facilitate access to health services.

Emilie Bruzelius, Matthew Le, Avi Kenny, Jordan Downey, Matteo Danieletto, Aaron Baum, Patrick Doupe, Bruno Silva, Philip J. Landrigan, Prabhjot Singh

J. Am. Medical Informatics Assoc., 2019

Revisiting the Evaluation of Theory of Mind through Question Answering.

Matthew Le, Y-Lan Boureau, Maximilian Nickel

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2016

Revisiting software transactional memory in Haskell.

Matthew Le, Ryan Yates, Matthew Fluet

Proceedings of the 9th International Symposium on Haskell, 2016

2015

Partial aborts for transactions via first-class continuations.

Matthew Le, Matthew Fluet

Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming, 2015

2014

Combining Shared State with Speculative Parallelism in a Functional Language.

Matthew Le, Matthew Fluet

Proceedings of the 26th 2014 International Symposium on Implementation and Application of Functional Languages, 2014

A Compiler Extension for Parallel Matrix Programming.

Kevin Williams, Matthew Le, Ted Kaminski, Eric Van Wyk

Proceedings of the 43rd International Conference on Parallel Processing, 2014

Practical and effective higher-order optimizations.

Lars Bergstrom, Matthew Fluet, Matthew Le, John H. Reppy, Nora Sandler

Proceedings of the 19th ACM SIGPLAN international conference on Functional programming, 2014

Spatio-Temporal Consistency as a Means to Identify Unlabeled Objects in a Continuous Data Field.

James H. Faghmous, Hung Nguyen, Matthew Le, Vipin Kumar

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics.

James H. Faghmous, Matthew Le, Muhammed Uluyol, Vipin Kumar, Snigdhansu Chatterjee

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Multiple Hypothesis Object Tracking For Unsupervised Self-Learning: An Ocean Eddy Tracking Application.

James H. Faghmous, Muhammed Uluyol, Luke Styles, Matthew Le, Varun Mithal, Shyam Boriah, Vipin Kumar

Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013