Learning Compositional Rules via Neural Program Synthesis

Authors: Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden M. Lake

NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental 4 Experiments, Table 1: Accuracy on SCAN splits., Table 2: Accuracy on few-shot number-word learning, using a maximum timeout of 45 seconds.
Researcher Affiliation Collaboration Maxwell I. Nye MIT Armando Solar-Lezama MIT Joshua B. Tenenbaum MIT Brenden M. Lake NYU Facebook AI
Pseudocode No The paper does not contain explicitly labeled 'Pseudocode' or 'Algorithm' blocks.
Open Source Code Yes Correspondence to mnye@mit.edu. Code can be found here: github.com/mtensor/rulesynthesis
Open Datasets Yes Our first experimental domain is the paradigm introduced in [13], informally dubbed Mini SCAN. Our next experiments concern the SCAN dataset [4, 5].
Dataset Splits No The paper describes training and test sets and support sets for various experiments, but does not explicitly mention or specify details for a 'validation' dataset split.
Hardware Specification No The paper mentions 'compute details in supplemental Section A.1' but does not provide specific hardware details in the main text.
Software Dependencies No The paper mentions 'pyprob probabilistic programming library' but does not specify its version number or any other software dependencies with version numbers.
Experiment Setup Yes In our experiments, the meta-grammar randomly sampled grammars with 3-4 primitive rules and 2-4 higher-order rules... For each grammar, we trained with a support set of 10-20 randomly sampled examples. Our synthesis methods were tested by sampling from the network for the best grammar, or until a candidate grammar was found which was consistent with all of the support examples, using a timeout of 30 sec (on one GPU; compute details in supplemental Section A.1). If no satisfying grammar is found within a set timeout of 20 seconds, we resample another 100 support examples and retry searching for a grammar.