Parametrized Hierarchical Procedures for Neural Programming
Authors: Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, Ion Stoica
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show in two benchmarks, Nano Craft and long-hand addition, that PHPs can learn neural programs more accurately from smaller amounts of both annotated and unannotated demonstrations. |
| Researcher Affiliation | Academia | Roy Fox, Richard Shin, Sanjay Krishnan, Ken Goldberg, Dawn Song, and Ion Stoica Department of Electrical Engineering and Computer Sciences University of California, Berkeley {royf,ricshin,sanjaykrishnan,goldberg,dawnsong,istoica}@berkeley.edu |
| Pseudocode | No | The paper describes algorithms in text and mathematical formulations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing open-source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our proposed method on the two settings studied by Li et al. (2017): Nano Craft, which involves an agent interacting in a grid world, and long-hand addition, which was also considered by Reed & De Freitas (2016) and Cai et al. (2017). Following Li et al. (2017), we trained our model on execution traces for inputs of each length 1 to 10. We used 16 traces for each input length, for a total of 160 traces. The dataset was generated randomly, but constrained to contain at least 1 example of each column of digits. |
| Dataset Splits | No | The paper mentions 'test performance' and 'test accuracy' but does not provide specific percentages or counts for training, validation, and test splits, nor does it refer to predefined validation splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., exact GPU/CPU models, processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers. |
| Experiment Setup | Yes | We trained each level for 2000 iterations, iteratively from the lowest level to the highest. The results are averaged over 5 trials with independent datasets. |