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ORCIDMatthew J. Johnson 0002
Person information
- affiliation: Google Brain
- affiliation: Massachusetts Institute of Technology, CSAIL
Other persons with the same name
- Matt Johnson 0001 (aka: Matthew Johnson 0001, Matthew J. Johnson 0001) — Florida Institute for Human and Machine Cognition (IHMC), Pensacola, USA
- Matthew J. Johnson 0003 — University of Cambridge, UK
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2020 – today
- 2023
[j5]Alexey Radul
, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin:
You Only Linearize Once: Tangents Transpose to Gradients. Proc. ACM Program. Lang. 7(POPL): 1246-1274 (2023)- 2022
- [c15]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:
Unified Scaling Laws for Routed Language Models. ICML 2022: 4057-4086 - [i14]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:
Unified Scaling Laws for Routed Language Models. CoRR abs/2202.01169 (2022) - [i13]Alexey Radul, Adam Paszke, Roy Frostig, Matthew J. Johnson, Dougal Maclaurin:
You Only Linearize Once: Tangents Transpose to Gradients. CoRR abs/2204.10923 (2022) - 2021
- [j4]Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin:
Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. Proc. ACM Program. Lang. 5(ICFP): 1-29 (2021) - [c14]Adam Paszke, Matthew J. Johnson, Roy Frostig, Dougal Maclaurin:
Parallelism-preserving automatic differentiation for second-order array languages. FHPNC@ICFP 2021: 13-23 - [i12]Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin:
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming. CoRR abs/2104.05372 (2021) - [i11]Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul:
Decomposing reverse-mode automatic differentiation. CoRR abs/2105.09469 (2021) - [i10]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:
Scaling Language Models: Methods, Analysis & Insights from Training Gopher. CoRR abs/2112.11446 (2021) - 2020
- [c13]Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud:
Learning Differential Equations that are Easy to Solve. NeurIPS 2020 - [i9]Jacob Kelly, Jesse Bettencourt, Matthew James Johnson, David Duvenaud:
Learning Differential Equations that are Easy to Solve. CoRR abs/2007.04504 (2020)
2010 – 2019
- 2019
- [c12]Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson:
The LORACs Prior for VAEs: Letting the Trees Speak for the Data. AISTATS 2019: 3292-3301 - [c11]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. ICML 2019: 7444-7453 - 2018
- [c10]Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams:
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS 2018: 1309-1317 - [i8]Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning. CoRR abs/1808.09105 (2018) - [i7]Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson:
The LORACs prior for VAEs: Letting the Trees Speak for the Data. CoRR abs/1810.06891 (2018) - [i6]Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous:
Simple, Distributed, and Accelerated Probabilistic Programming. CoRR abs/1811.02091 (2018) - [i5]Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran:
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. CoRR abs/1811.11926 (2018) - 2017
- [c9]Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski:
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS 2017: 914-922 - [c8]Scott W. Linderman, Matthew J. Johnson:
Structure-Exploiting variational inference for recurrent switching linear dynamical systems. CAMSAP 2017: 1-5 - [i4]Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams:
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. CoRR abs/1704.04997 (2017) - 2016
- [j3]Elaine Angelino, Matthew James Johnson, Ryan P. Adams:
Patterns of Scalable Bayesian Inference. Found. Trends Mach. Learn. 9(2-3): 119-247 (2016) - [j2]Huseyin Melih Elibol, Vincent Nguyen, Scott W. Linderman, Matthew J. Johnson, Amna Hashmi, Finale Doshi-Velez:
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. J. Mach. Learn. Res. 17: 133:1-133:38 (2016) - [c7]Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams:
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML 2016: 2682-2691 - [c6]Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta:
Composing graphical models with neural networks for structured representations and fast inference. NIPS 2016: 2946-2954 - 2015
- [c5]Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams:
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. NIPS 2015: 3456-3464 - [i3]Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson:
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR abs/1501.00052 (2015) - 2014
- [b1]Matthew James Johnson:
Bayesian time series models and scalable inference. Massachusetts Institute of Technology, Cambridge, MA, USA, 2014 - [c4]Matthew James Johnson, Alan S. Willsky:
Stochastic Variational Inference for Bayesian Time Series Models. ICML 2014: 1854-1862 - 2013
- [j1]Matthew J. Johnson, Alan S. Willsky:
Bayesian nonparametric hidden semi-Markov models. J. Mach. Learn. Res. 14(1): 673-701 (2013) - [c3]Matthew J. Johnson, James Saunderson, Alan S. Willsky:
Analyzing Hogwild Parallel Gaussian Gibbs Sampling. NIPS 2013: 2715-2723 - 2012
- [i2]Matthew J. Johnson, Alan S. Willsky:
The Hierarchical Dirichlet Process Hidden Semi-Markov Model. CoRR abs/1203.3485 (2012) - [i1]Matthew James Johnson:
A Simple Explanation of A Spectral Algorithm for Learning Hidden Markov Models. CoRR abs/1204.2477 (2012) - 2010
- [c2]Vincent Y. F. Tan, Matthew J. Johnson, Alan S. Willsky:
Necessary and sufficient conditions for high-dimensional salient feature subset recovery. ISIT 2010: 1388-1392 - [c1]Matthew J. Johnson, Alan S. Willsky:
The Hierarchical Dirichlet Process Hidden Semi-Markov Model. UAI 2010: 252-259
Coauthor Index
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