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Luke Hewitt
Luke Hewitt
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Dreamcoder: growing generalizable, interpretable knowledge with wake–sleep bayesian program learning
K Ellis, L Wong, M Nye, M Sable-Meyer, L Cary, L Anaya Pozo, L Hewitt, ...
Philosophical Transactions of the Royal Society A 381 (2251), 20220050, 2023
1822023
Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning
K Ellis, C Wong, M Nye, M Sablé-Meyer, L Morales, L Hewitt, L Cary, ...
Proceedings of the 42nd acm sigplan international conference on programming …, 2021
1492021
Learning to infer program sketches
M Nye, L Hewitt, J Tenenbaum, A Solar-Lezama
International Conference on Machine Learning, 4861-4870, 2019
1152019
The variational homoencoder: Learning to learn high capacity generative models from few examples
LB Hewitt, MI Nye, A Gane, T Jaakkola, JB Tenenbaum
arXiv preprint arXiv:1807.08919, 2018
742018
Quantifying the potential persuasive returns to political microtargeting
BM Tappin, C Wittenberg, LB Hewitt, AJ Berinsky, DG Rand
Proceedings of the National Academy of Sciences 120 (25), e2216261120, 2023
272023
Learning to learn generative programs with Memoised Wake-Sleep
L Hewitt, TA Le, J Tenenbaum
Conference on Uncertainty in Artificial Intelligence, 1278-1287, 2020
242020
System and method for inputting images or labels into electronic devices
J Aley, G Jones, L Hewitt
US Patent 10,664,657, 2020
232020
Emotion prediction as computation over a generative theory of mind
SD Houlihan, M Kleiman-Weiner, LB Hewitt, JB Tenenbaum, R Saxe
Philosophical Transactions of the Royal Society A 381 (2251), 20220047, 2023
142023
Estimating the persistence of party cue influence in a panel survey experiment
BM Tappin, LB Hewitt
Journal of Experimental Political Science 10 (1), 50-61, 2023
112023
Auditory scene analysis as Bayesian inference in sound source models
M Cusimano, L Hewitt, JB Tenenbaum, JH McDermott
Cognitive Computational Neuroscience, 2018
102018
How experiments help campaigns persuade voters: Evidence from a large archive of campaigns’ own experiments
L Hewitt, D BROOCKMAN, A Coppock, BENM TAPPIN, J Slezak, ...
American Political Science Review, 1-19, 2024
42024
Rank-heterogeneous effects of political messages: Evidence from randomized survey experiments testing 59 video treatments
L Hewitt, BM Tappin
PsyArXiv, 2022
42022
Hybrid memoised wake-sleep: approximate inference at the discrete-continuous interface
TA Le, KM Collins, L Hewitt, K Ellis, N Siddharth, SJ Gershman, ...
arXiv preprint arXiv:2107.06393, 2021
42021
WOLFE: an nlp-friendly declarative machine learning stack
S Singh, T Rocktäschel, L Hewitt, J Naradowsky, S Riedel
Proceedings of the 2015 Conference of the North American Chapter of the …, 2015
42015
Designing an IDE for probabilistic programming: Challenges and a prototype
S Singh, S Riedel, L Hewitt, T Rocktäschel
Advances in neural information processing systems workshop on probabilistic …, 2014
22014
Bayesian auditory scene synthesis explains human perception of illusions and everyday sounds
M Cusimano, LB Hewitt, JH McDermott
bioRxiv, 2023.04. 27.538626, 2023
12023
Inferring Structured Visual Concepts from Minimal Data.
P Qian, LB Hewitt, J Tenenbaum, R Levy
CogSci, 2620-2626, 2019
12019
Using in-survey randomized controlled trials to support future pandemic response
BM Tappin, L Hewitt
OSF, 2024
2024
What's at stake in political messaging?
L Hewitt
Massachusetts Institute of Technology, 2022
2022
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
M Sablé-Meyer, L Cary, L Morales, L Hewitt, A Solar-Lezama, ...
arXiv preprint arXiv:2006.08381, 2020
2020
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