Jan-Willem van de Meent

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Frank WoodUniversity of British ColumbiaVerified email at cs.ubc.ca
Brooks PaigeAssociate Professor, University College LondonVerified email at ucl.ac.uk
Chris H WigginsAssociate Professor, Columbia UniversityVerified email at columbia.edu
Byron WallaceAssociate Professor, Northeastern UniversityVerified email at ccs.neu.edu
Jennifer DyElectrical and Computer Engineering, Northeastern UniversityVerified email at ece.neu.edu
Hao WuNortheastern UniversityVerified email at northeastern.edu
Dana BrooksResearch Professor / Professor Emeritus of Electrical and Computer Engineering, NortheasternVerified email at ece.neu.edu
David TolpinOfftopiaVerified email at robots.ox.ac.uk
Raymond E. GoldsteinUniversity of CambridgeVerified email at damtp.cam.ac.uk
Hongseok YangProfessor, School of Computing, KAISTVerified email at kaist.ac.kr
Alican BozkurtPaige.AIVerified email at paige.ai
Tom RainforthUniversity of OxfordVerified email at stats.ox.ac.uk
Vikash K. MansinghkaMIT, Probabilistic Computing ProjectVerified email at mit.edu
Heiko ZimmermannUniversity of AmsterdamVerified email at uva.nl
Ondrej BizaNortheastern UniversityVerified email at northeastern.edu
N. SiddharthReader in Explainable AI, University of EdinburghVerified email at iffsid.com
Robin WaltersNortheasternVerified email at northeastern.edu
martin van heckeLeiden University / AmolfVerified email at amolf.nl
Jered McInerneyNortheastern University, CodaMetrixVerified email at northeastern.edu
Lisa Feldman BarrettUniversity Distinguished Professor of Psychology, Northeastern UniversityVerified email at affective-science.org

Jan-Willem van de Meent

University of Amsterdam and Northeastern University

Verified email at uva.nl - Homepage

Probabilistic Programming Machine Learning Artificial Intelligence Data Science


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Learning Disentangled Representations with Semi-Supervised Deep Generative Models S N, B Paige, JW van de Meent, A Desmaison, N Goodman, P Kohli, ... Advances in Neural Information Processing Systems 30, 2017	405*	2017
A new approach to probabilistic programming inference F Wood, JW Meent, V Mansinghka Artificial intelligence and statistics, 1024-1032, 2014	403	2014
An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018	207	2018
Universal and wide shear zones in granular bulk flow D Fenistein, JW van de Meent, M van Hecke Physical review letters 92 (9), 094301, 2004	191	2004
Empirical Bayes methods enable advanced population-level analyses of single-molecule FRET experiments JW van de Meent, JE Bronson, CH Wiggins, RL Gonzalez Biophysical journal 106 (6), 1327-1337, 2014	188	2014
Structured disentangled representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	171*	2019
A physical perspective on cytoplasmic streaming RE Goldstein, JW Van De Meent Interface focus 5 (4), 20150030, 2015	166	2015
Design and implementation of probabilistic programming language anglican D Tolpin, JW van de Meent, H Yang, F Wood Proceedings of the 28th Symposium on the Implementation and Application of …, 2016	149	2016
Microfluidics of cytoplasmic streaming and its implications for intracellular transport RE Goldstein, I Tuval, JW van de Meent Proceedings of the National Academy of Sciences 105 (10), 3663-3667, 2008	141	2008
Enhancing few-shot image classification with unlabelled examples P Bateni, J Barber, JW Van de Meent, F Wood Proceedings of the IEEE/CVF winter conference on applications of computer …, 2022	74	2022
Interoception as modeling, allostasis as control E Sennesh, J Theriault, D Brooks, JW van de Meent, LF Barrett, ... Biological Psychology 167, 108242, 2022	72	2022
Core precession and global modes in granular bulk flow D Fenistein, JW van de Meent, M van Hecke Physical review letters 96 (11), 118001, 2006	70	2006
Measurement of cytoplasmic streaming in single plant cells by magnetic resonance velocimetry JW van de Meent, AJ Sederman, LF Gladden, RE Goldstein Journal of Fluid Mechanics 642, 5-14, 2010	55	2010
Nature’s microfluidic transporter: rotational cytoplasmic streaming at high Péclet numbers JW van de Meent, I Tuval, RE Goldstein Physical review letters 101 (17), 178102, 2008	55	2008
Probabilistic programming in Anglican D Tolpin, JW van de Meent, F Wood Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015	54	2015
Learning symmetric embeddings for equivariant world models JY Park, O Biza, L Zhao, JW van de Meent, R Walters arXiv preprint arXiv:2204.11371, 2022	42	2022
Interacting particle markov chain monte carlo T Rainforth, C Naesseth, F Lindsten, B Paige, JW Vandemeent, A Doucet, ... International Conference on Machine Learning, 2616-2625, 2016	41	2016
Learning disentangled representations of texts with application to biomedical abstracts S Jain, E Banner, JW van de Meent, IJ Marshall, BC Wallace Proceedings of the Conference on Empirical Methods in Natural Language …, 2018	36	2018
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data JW Meent, J Bronson, F Wood, R Gonzalez Jr, C Wiggins International Conference on Machine Learning, 361-369, 2013	36	2013
Bayesian optimization for probabilistic programs T Rainforth, TA Le, JW van de Meent, MA Osborne, F Wood Advances in Neural Information Processing Systems 29, 2016	34	2016

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