Liyi Zhang

20212022202320242 7 8 15

Public access

1 article

0 articles

available

not available

Based on funding mandates

Christian A. NaessethAssistant Professor, University of AmsterdamVerified email at uva.nl
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Thomas L. GriffithsProfessor of Psychology and Computer Science, Princeton UniversityVerified email at princeton.edu
Antonio Khalil MorettiSpelman CollegeVerified email at cs.columbia.edu
Itsik Pe'erDepartment of Computer Science, Columbia UniversityVerified email at cs.columbia.edu
Theodore SumersAnthropicVerified email at anthropic.com
R. Thomas McCoyAssistant Professor in Linguistics, Yale UniversityVerified email at yale.edu
Jian-Qiao ZhuDepartment of Computer Science, Princeton UniversityVerified email at princeton.edu
Gianluca BencomoGraduate Student, Princeton UniversityVerified email at princeton.edu
Sreejan KumarPhD Candidate, Princeton UniversityVerified email at princeton.edu
Jake C. SnellPrinceton UniversityVerified email at princeton.edu
Raja MarjiehPhD Candidate, Princeton UniversityVerified email at princeton.edu
Declan CampbellGraduate Student, Princeton Neuroscience InstituteVerified email at princeton.edu

Liyi Zhang

Verified email at princeton.edu - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Variational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inference AK Moretti, L Zhang, CA Naesseth, H Venner, D Blei, I Pe’er Uncertainty in Artificial Intelligence, 971-981, 2021	18	2021
Deep de Finetti: Recovering topic distributions from large language models L Zhang, RT McCoy, TR Sumers, JQ Zhu, TL Griffiths arXiv preprint arXiv:2312.14226, 2023	6	2023
Transport score climbing: Variational inference using forward KL and adaptive neural transport L Zhang, DM Blei, CA Naesseth Transactions on Machine Learning Research, 2022	6	2022
Variational combinatorial sequential monte carlo for bayesian phylogenetic inference A Moretti, L Zhang, I Pe’er Machine Learning in Computational Biology, 2020	2	2020
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions L Zhang, MY Li, TL Griffiths arXiv preprint arXiv:2406.03707, 2024	1	2024
Analyzing the Benefits of Prototypes for Semi-Supervised Category Learning L Zhang, L Nelson, TL Griffiths Proceedings of the Annual Meeting of the Cognitive Science Society, 2024		2024
Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases R Marjieh, S Kumar, D Campbell, L Zhang, G Bencomo, J Snell, ... arXiv preprint arXiv:2405.19420, 2024		2024

The system can't perform the operation now. Try again later.

Articles 1–7

Citations per year