Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction
Authors: Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Josh Tenenbaum
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain. ... We evaluated EC2 on random 50/50 test/train split. ... Tbl. 3 compares our model against these alternatives. ... Figure 4: Learning curves for EC2 both with (in orange) and without (in teal) the recognition model. |
| Researcher Affiliation | Academia | Kevin Ellis MIT ellisk@mit.edu Lucas Morales MIT lucasem@mit.edu Mathias Sablé-Meyer ENS Paris-Saclay mathsm@mit.edu Armando Solar-Lezama MIT asolar@csail.mit.edu Joshua B. Tenenbaum MIT jbt@mit.edu |
| Pseudocode | Yes | Algorithm 1 The EC2 Algorithm |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We tested, but did not train, on the 108 text editing problems from the Sy Gu S [32] program synthesis competition. [32] Rajeev Alur, Dana Fisman, Rishabh Singh, and Armando Solar-Lezama. Sygus-comp 2016: results and analysis. ar Xiv preprint ar Xiv:1611.07627, 2016. |
| Dataset Splits | Yes | We evaluated EC2 on random 50/50 test/train split. ... We tested, but did not train, on the 108 text editing problems from the Sy Gu S [32] program synthesis competition. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or cloud computing instance types used for experiments. |
| Software Dependencies | No | The paper mentions using a 'bidirectional GRU [30]' but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | Input: Initial DSL D, set of tasks X, iterations I Hyperparameters: Enumeration timeout T Initialize θ uniform. ... After three iterations, it assembles a DSL... |