Jan-Willem van de Meent
Jan-Willem van de Meent
Northeastern University
Verified email at northeastern.edu - Homepage
TitleCited byYear
A new approach to probabilistic programming inference
F Wood, JW Meent, V Mansinghka
Artificial Intelligence and Statistics, 1024-1032, 2014
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
Learning disentangled representations with semi-supervised deep generative models
S Narayanaswamy, TB Paige, JW Van de Meent, A Desmaison, ...
Advances in Neural Information Processing Systems, 5925-5935, 2017
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
Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments
JW van de Meent, JE Bronson, CH Wiggins, RL Gonzalez Jr
Biophysical Journal 106 (March), 1327-37, 2014
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
A physical perspective on cytoplasmic streaming
RE Goldstein, JW van de Meent
Interface focus 5 (4), 20150030, 2015
Design and implementation of probabilistic programming language anglican
D Tolpin, JW van de Meent, H Yang, F Wood
arXiv preprint arXiv:1608.05263, 2016
Structured Disentangled Representations
B Esmaeili, H Wu, S Jain, A Bozkurt, N Siddharth, B Paige, DH Brooks, ...
arXiv preprint arXiv:1804.02086, 2018
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
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
Probabilistic programming in Anglican
D Tolpin, JW van de Meent, F Wood
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015
Bayesian optimization for probabilistic programs
T Rainforth, TA Le, JW van de Meent, MA Osborne, F Wood
Advances in Neural Information Processing Systems, 280-288, 2016
An introduction to probabilistic programming
JW van de Meent, B Paige, H Yang, F Wood
arXiv preprint arXiv:1809.10756, 2018
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
JW van de Meent, JE Bronson, F Wood, RL Gonzalez Jr, CH Wiggins
arXiv preprint arXiv:1305.3640, 2013
Black-box policy search with probabilistic programs
JW Van De Meent, B Paige, D Tolpin, F Wood
Journal of Machine Learning Research, 2016
Particle Gibbs with Ancestor Sampling for Probabilistic Programs.
JW van de Meent, H Yang, V Mansinghka, FD Wood
Multiple LacI-mediated loops revealed by Bayesian statistics and tethered particle motion
S Johnson, JW van de Meent, R Phillips, CH Wiggins, M Lindén
Nucleic acids research 42 (16), 10265-10277, 2014
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
Stylistic clusters and the Syrian/South Syrian tradition of first-millennium BCE Levantine ivory carving: a machine learning approach
AR Gansell, JW van de Meent, S Zairis, CH Wiggins
Journal of Archaeological Science 44, 194-205, 2014
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Articles 1–20