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From Perception to Programs: Regularize, Overparameterize, and Amortize.

, and . ICML, volume 202 of Proceedings of Machine Learning Research, page 33616-33631. PMLR, (2023)

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Sampling for Bayesian Program Learning., , and . NIPS, page 1289-1297. (2016)CrossBeam: Learning to Search in Bottom-Up Program Synthesis., , , and . ICLR, OpenReview.net, (2022)LLM-Guided Probabilistic Program Induction for POMDP Model Estimation., , , , , , and . CoRR, (May 2025)Learning to Learn Programs from Examples: Going Beyond Program Structure., and . IJCAI, page 1638-1645. ijcai.org, (2017)DeepSynth: Scaling Neural Program Synthesis with Distribution-based Search., , , and . J. Open Source Softw., 7 (78): 4151 (October 2022)Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction., , , , and . NeurIPS, page 7816-7826. (2018)Learning to Infer Graphics Programs from Hand-Drawn Images., , , and . NeurIPS, page 6062-6071. (2018)Scaling Neural Program Synthesis with Distribution-Based Search., , , , , and . AAAI, page 6623-6630. AAAI Press, (2022)Leveraging Language to Learn Program Abstractions and Search Heuristics., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11193-11204. PMLR, (2021)From Perception to Programs: Regularize, Overparameterize, and Amortize., and . ICML, volume 202 of Proceedings of Machine Learning Research, page 33616-33631. PMLR, (2023)