Top-Down Synthesis for Library Learning

29 November 2022

Papers citing "Top-Down Synthesis for Library Learning"

7 / 7 papers shown

Title
MeMo: Meaningful, Modular Controllers via Noise Injection Megan Tjandrasuwita Jie Xu Armando Solar-Lezama Wojciech Matusik 21 0 0 24 May 2024
ReGAL: Refactoring Programs to Discover Generalizable Abstractions Elias Stengel-Eskin Archiki Prasad Mohit Bansal 25 13 0 29 Jan 2024
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives R. K. Jones Paul Guerrero Niloy J. Mitra Daniel E. Ritchie 21 23 0 09 May 2023
Anti-unification and Generalization: A Survey David M. Cerna Temur Kutsia AI4CE 23 17 0 01 Feb 2023
Neurosymbolic Programming for Science Jennifer J. Sun Megan Tjandrasuwita Atharva Sehgal Armando Solar-Lezama Swarat Chaudhuri Yisong Yue Omar Costilla-Reyes NAI 39 12 0 10 Oct 2022
Learning Differentiable Programs with Admissible Neural Heuristics Ameesh Shah Eric Zhan Jennifer J. Sun Abhinav Verma Yisong Yue Swarat Chaudhuri 140 43 0 23 Jul 2020
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning Kevin Ellis Catherine Wong Maxwell Nye Mathias Sablé-Meyer Luc Cary Lucas Morales Luke B. Hewitt Armando Solar-Lezama J. Tenenbaum NAI CLL 22 190 0 15 Jun 2020