Learning Libraries of Subroutines for Neurally-Guided Bayesian Program Induction

By Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum. 2018

81 citations recorded by the source corpus.

Subfields: Code Generation & Program Synthesis

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In Code Generation & Program Synthesis

Citations in the corpus

Program Synthesis with Large Language Models (2021, methodology): “…a recent resurgence of interest in techniques (both symbolic and ‘neuro-symbolic’) for synthesizing programs [Balog et al., 2017, Devlin et al., 2017, Ellis et al., 2018, 2020, Odena et al., 2020], but these techniques have largely been applied to restricted domain-specific languages (DSLs)…”
Large Language Models Are Human-Level Prompt Engineers (2022, methodology): “In contrast to prior approaches that require a suitable structured hypothesis space and library of components (Liang et al., 2010; Ellis et al., 2018), we leverage the structure provided by LLMs to search over the space of natural language programs.”
DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “[46] use a neural network to efficiently search over a well-designed DSL.”
Write, Execute, Assess: Program Synthesis with a REPL (2019, methodology): “Integrating learning and search to tackle the problem of program synthesis is an old idea experiencing a recent resurgence [2, 3, 4, 5, 6, 7, 8, 9, 10, 11].”
Learning to Represent Programs with Property Signatures (2020, methodology): “Deep learning methods have shown promise at automatically generating programs from a small set of input-output examples (Balog et al., 2016; Devlin et al., 2017; Ellis et al., 2018b; 2019b).”
Neurosymbolic Programming (2021, methodology): “A second category of methods, which we call learning to specify (Ellis et al., 2018b), determine how to generalize incomplete or ambiguous task specifications to more complete specifications.”
Program Synthesis and Semantic Parsing with Learned Code Idioms (2019, background): “[13] showed that such DSLs are learnable in the classic domains of inductive program synthesis; in this work, we target general-purpose code generation, where DSL design is difficult even for experts.”
Leveraging Language to Learn Program Abstractions and Search Heuristics (2021, background): “…Abolafia et al., 2018; Nye et al., 2019; Ellis et al., 2019; Si et al., 2019; Ye et al., 2020a); and learn libraries of symbolic abstractions from a collection of related programs or tasks (Dechter et al., 2013; Zhang et al., 2017; Shin et al., 2019; Dumancić & Cropper; Ellis et al., 2018; 2021).”
Playgol: learning programs through play (2019, methodology): “In other words, our approach allows a learner to discover highly reusable concepts without a user-supplied corpus of training tasks, which Ellis et al. [Ellis et al. , 2018a] argue is essential for program induction to become a standard part of the AI toolkit.”