Long Ouyang
Long Ouyang
Department of Psychology, Stanford University
Zweryfikowany adres z stanford.edu
Tytuł
Cytowane przez
Cytowane przez
Rok
Learning to summarize from human feedback
N Stiennon, L Ouyang, J Wu, DM Ziegler, R Lowe, C Voss, A Radford, ...
arXiv preprint arXiv:2009.01325, 2020
432020
Practical optimal experiment design with probabilistic programs
L Ouyang, MH Tessler, D Ly, N Goodman
arXiv preprint arXiv:1608.05046, 2016
142016
Semantic coherence facilitates distributional learning
L Ouyang, L Boroditsky, MC Frank
Cognitive science 41, 855-884, 2017
112017
Fabular: Regression formulas as probabilistic programming
J Borgström, AD Gordon, L Ouyang, C Russo, A Ścibior, M Szymczak
Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of …, 2016
82016
webppl-oed: A practical optimal experiment design system.
L Ouyang, MH Tessler, D Ly, ND Goodman
CogSci, 2018
52018
Semantic Coherence Facilitates Distributional Learning of Word Meanings
L Ouyang, L Boroditsky, MC Frank
Proceedings of the 34th Annual Meeting of the Cognitive Science Society, 2012
32012
Recursively summarizing books with human feedback
J Wu, L Ouyang, DM Ziegler, N Stiennon, R Lowe, J Leike, P Christiano
arXiv preprint arXiv:2109.10862, 2021
12021
Pedagogical learning
L Ouyang, MC Frank
arXiv preprint arXiv:1711.09401, 2017
12017
Bayesian Inference of Regular Expressions from Human-Generated Example Strings
L Ouyang
arXiv preprint arXiv:1805.08427, 2018
2018
The Effect of Learning on Learning
L Ouyang
Stanford University, 2015
2015
Support and influence analysis for visualizing posteriors of probabilistic programs
L Ouyang
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–11