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
Babak EsmaeiliUniversity of AmsterdamVerified email at uva.nl
Chris H WigginsAssociate Professor, Columbia UniversityVerified email at columbia.edu
Hao WuNortheastern UniversityVerified email at northeastern.edu
Jennifer DyElectrical and Computer Engineering, Northeastern UniversityVerified email at ece.neu.edu
David TolpinOfftopiaVerified email at robots.ox.ac.uk
Raymond E. GoldsteinUniversity of CambridgeVerified email at damtp.cam.ac.uk
Dana BrooksResearch Professor / Professor Emeritus of Electrical and Computer Engineering, NortheasternVerified email at ece.neu.edu
Hongseok YangProfessor, School of Computing, KAISTVerified email at kaist.ac.kr
Vikash K. MansinghkaMIT, Probabilistic Computing ProjectVerified email at mit.edu
Byron WallaceAssociate Professor, Northeastern UniversityVerified email at ccs.neu.edu
Alican BozkurtDepartment of Electrical and Computer Engineering, Northeastern UniversityVerified email at northeastern.edu
Tom RainforthFlorence Nightingale Bicentennial Fellow, University of OxfordVerified email at stats.ox.ac.uk
N. SiddharthReader in Explainable AI, University of EdinburghVerified email at iffsid.com
martin van heckeLeiden University / AmolfVerified email at amolf.nl
Heiko ZimmermannUniversity of AmsterdamVerified email at uva.nl
Ondrej BizaNortheastern UniversityVerified email at northeastern.edu
Ajay SatputeNortheastern UniversityVerified email at northeastern.edu
J. Benjamin HutchinsonUniversity of OregonVerified email at uoregon.edu

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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A new approach to probabilistic programming inference F Wood, JW Meent, V Mansinghka Artificial intelligence and statistics, 1024-1032, 2014	358	2014
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	333*	2017
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	183	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	146	2014
An introduction to probabilistic programming JW van de Meent, B Paige, H Yang, F Wood arXiv preprint arXiv:1809.10756, 2018	144	2018
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	126*	2019
A physical perspective on cytoplasmic streaming RE Goldstein, JW van de Meent Interface focus 5 (4), 20150030, 2015	120	2015
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	120	2008
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	110	2016
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	73	2006
Probabilistic programming in Anglican D Tolpin, JW van de Meent, F Wood Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015	49	2015
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	48	2008
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	44	2010
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	39	2016
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	35	2016
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	34	2022
Structured disentangled representations B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ... arXiv preprint arXiv:1804.02086, 2018	31	2018
Particle Gibbs with ancestor sampling for probabilistic programs JW Meent, H Yang, V Mansinghka, F Wood Artificial Intelligence and Statistics, 986-994, 2015	29	2015
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	28	2013
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	27	2018

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