Curtis Hawthorne

According to our database, Curtis Hawthorne authored at least 21 papers between 2018 and 2023.

Collaborative distances:

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

2023

Scaling Up Models and Data with t5x and seqio.

Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo

J. Mach. Learn. Res., 2023

2022

Continuous diffusion for categorical data.

Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Leblond, Will Grathwohl, Jonas Adler

CoRR, 2022

The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling.

Yusong Wu, Josh Gardner, Ethan Manilow, Ian Simon, Curtis Hawthorne, Jesse H. Engel

CoRR, 2022

Scaling Up Models and Data with t5x and seqio.

Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo

CoRR, 2022

Scaling Polyphonic Transcription with Mixtures of Monophonic Transcriptions.

Ian Simon, Josh Gardner, Curtis Hawthorne, Ethan Manilow, Jesse H. Engel

Proceedings of the 23rd International Society for Music Information Retrieval Conference, 2022

Multi-instrument Music Synthesis with Spectrogram Diffusion.

Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, Jesse H. Engel

Proceedings of the 23rd International Society for Music Information Retrieval Conference, 2022

General-purpose, long-context autoregressive modeling with Perceiver AR.

Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel

Proceedings of the International Conference on Machine Learning, 2022

MT3: Multi-Task Multitrack Music Transcription.

Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse H. Engel

Proceedings of the Tenth International Conference on Learning Representations, 2022

Improving Source Separation by Explicitly Modeling Dependencies between Sources.

Ethan Manilow, Curtis Hawthorne, Cheng-Zhi Anna Huang, Bryan Pardo, Jesse H. Engel

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Symbolic Music Generation with Diffusion Models.

Gautam Mittal, Jesse H. Engel, Curtis Hawthorne, Ian Simon

Proceedings of the 22nd International Society for Music Information Retrieval Conference, 2021

Sequence-to-Sequence Piano Transcription with Transformers.

Curtis Hawthorne, Ian Simon, Rigel Swavely, Ethan Manilow, Jesse H. Engel

Proceedings of the 22nd International Society for Music Information Retrieval Conference, 2021

2020

Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset.

Lee Callender, Curtis Hawthorne, Jesse H. Engel

CoRR, 2020

Encoding Musical Style with Transformer Autoencoders.

Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse H. Engel

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

The Bach Doodle: Approachable music composition with machine learning at scale.

Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts, Monica Dinculescu, James Wexler, Leon Hong, Jacob Howcroft

CoRR, 2019

Approachable Music Composition with Machine Learning at Scale.

Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts, Monica Dinculescu, James Wexler, Leon Hong, Jacob Howcroft

Proceedings of the 20th International Society for Music Information Retrieval Conference, 2019

Music Transformer: Generating Music with Long-Term Structure.

Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck

Proceedings of the 7th International Conference on Learning Representations, 2019

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset.

Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse H. Engel, Douglas Eck

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation.

Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Douglas Eck

CoRR, 2018

Learning a Latent Space of Multitrack Measures.

Ian Simon, Adam Roberts, Colin Raffel, Jesse H. Engel, Curtis Hawthorne, Douglas Eck

CoRR, 2018

Onsets and Frames: Dual-Objective Piano Transcription.

Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse H. Engel, Sageev Oore, Douglas Eck

Proceedings of the 19th International Society for Music Information Retrieval Conference, 2018

A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music.

Adam Roberts, Jesse H. Engel, Colin Raffel, Curtis Hawthorne, Douglas Eck

Proceedings of the 35th International Conference on Machine Learning, 2018