Matthew D. Hoffman - Concepedia

Author

Matthew D. Hoffman

Also Known As

Esha Datta, Hoffman, Matt, Hoffman, Matthew, Hoffman, Matthew D., Hoffman, Matthew W., Langmore, Ian, M. D. Hoffman, M. David Hoffman, M. Hoffman, Matthew D. Hoffman

113

Publications

17.7K

Citations

H-Index

109

Concepts

All Affiliations

Adobe Systems (United States)Google (United States)Columbia University Princeton University Stanford University

About

Matthew D. Hoffman is an author at Adobe Systems (United States) specializing in engineering, computer science, and machine learning.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	29	40	7.5K
2	Management	3	3	205
3	Arts	2	2	145
4	Health Sciences	2	2	34
5	Public Health	1	1	32

Matthew D. Hoffman

Publications

17.5K

Citations

H-Index

Page 1

	Year	Citations
<i>Stan</i>: A Probabilistic Programming Language Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Journal of Statistical Software	2017	7.1K
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman, Andrew Gelman arXiv (Cornell University) EngineeringMachine LearningMonte Carlo MethodsStep Size εMarkov Chain Monte Carlo	2011	1.8K
Stochastic variational inference Matthew D. Hoffman, David M. Blei, Chong Wang, Journal of Machine Learning Research Bayesian StatisticEngineeringStochastic AnalysisStochastic PhenomenonBayesian Inference	2013	1.5K
Online Learning for Latent Dirichlet Allocation Matthew D. Hoffman, Francis Bach, David M. Blei	2010	1.3K
Stan: A Probabilistic Programming Language Bob Carpenter, Andrew Gelman, Matthew D. Hoffman,	2017	1.3K
Variational Autoencoders for Collaborative Filtering Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Natural Language ProcessingGroup RecommendersEngineeringMachine LearningData Science	2018	1.3K
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D’Amour, Katherine Heller, Dan Moldovan, arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningModern Ml PipelinesMachine Learning Tool	2020	430
Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt, Matthew D. Hoffman, David M. Blei Journal of Machine Learning Research Constant SgdMarkov Decision ProcessEngineeringMachine LearningStochastic Optimization	2017	360
Learning Activation Functions to Improve Deep Neural Networks Forest Agostinelli, Matthew D. Hoffman, Peter Sadowski, arXiv (Cornell University) Non-linear Activation FunctionConvolutional Neural NetworkDeep Neural NetworksGradient DescentMachine Learning	2014	349
Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view Paris Smaragdis, Cédric Févotte, Gautham J. Mysore, IEEE Signal Processing Magazine Mathematical ProgrammingSource SeparationEngineeringMachine LearningComplex Mixtures	2014	164

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