Matthew J. Johnson

According to our database, Matthew J. Johnson authored at least 29 papers between 2010 and 2023.

Collaborative distances:

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

2023

You Only Linearize Once: Tangents Transpose to Gradients.

Alexey Radul, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin

Proc. ACM Program. Lang., January, 2023

2022

Unified Scaling Laws for Routed Language Models.

Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J. Johnson, Katie Millican, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan

CoRR, 2022

Unified Scaling Laws for Routed Language Models.

Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew J. Johnson, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Marc'Aurelio Ranzato, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan

Proceedings of the International Conference on Machine Learning, 2022

2021

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming.

Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin

Proc. ACM Program. Lang., 2021

Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, H. Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J. Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving

CoRR, 2021

Decomposing reverse-mode automatic differentiation.

Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul

CoRR, 2021

Parallelism-preserving automatic differentiation for second-order array languages.

Adam Paszke, Matthew J. Johnson, Roy Frostig, Dougal Maclaurin

Proceedings of the FHPNC 2021: Proceedings of the 9th ACM SIGPLAN International Workshop on Functional High-Performance and Numerical Computing, 2021

2020

Learning Differential Equations that are Easy to Solve.

Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning.

Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine

Proceedings of the 36th International Conference on Machine Learning, 2019

The LORACs Prior for VAEs: Letting the Trees Speak for the Data.

Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language.

Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

CoRR, 2018

Simple, Distributed, and Accelerated Probabilistic Programming.

Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous

CoRR, 2018

SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning.

Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine

CoRR, 2018

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models.

Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Structure-Exploiting variational inference for recurrent switching linear dynamical systems.

Scott W. Linderman, Matthew J. Johnson

Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems.

Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders.

Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez

J. Mach. Learn. Res., 2016

Patterns of Scalable Bayesian Inference.

Elaine Angelino, Matthew James Johnson, Ryan P. Adams

Found. Trends Mach. Learn., 2016

Composing graphical models with neural networks for structured representations and fast inference.

Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM.

Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models.

Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson

CoRR, 2015

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation.

Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Bayesian time series models and scalable inference.

Matthew James Johnson

PhD thesis, 2014

Stochastic Variational Inference for Bayesian Time Series Models.

Matthew James Johnson, Alan S. Willsky

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Bayesian nonparametric hidden semi-Markov models.

Matthew J. Johnson, Alan S. Willsky

J. Mach. Learn. Res., 2013

Analyzing Hogwild Parallel Gaussian Gibbs Sampling.

Matthew J. Johnson, James Saunderson, Alan S. Willsky

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

A Simple Explanation of A Spectral Algorithm for Learning Hidden Markov Models

Matthew James Johnson

CoRR, 2012

2010

The Hierarchical Dirichlet Process Hidden Semi-Markov Model.

Matthew J. Johnson, Alan S. Willsky

Proceedings of the UAI 2010, 2010

Necessary and sufficient conditions for high-dimensional salient feature subset recovery.

Vincent Y. F. Tan, Matthew J. Johnson, Alan S. Willsky

Proceedings of the IEEE International Symposium on Information Theory, 2010