Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Neural Program Meta-Induction
Authors: Jacob Devlin, Rudy R. Bunel, Rishabh Singh, Matthew Hausknecht, Pushmeet Kohli
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. |
| Researcher Affiliation | Collaboration | Jacob Devlin Google EMAIL Rudy Bunel University of Oxford EMAIL Rishabh Singh Microsoft Research EMAIL Matthew Hausknecht Microsoft Research EMAIL Pushmeet Kohli Deep Mind EMAIL |
| Pseudocode | No | The paper describes its models and methods using textual descriptions and architectural diagrams (Figure 2), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository or mention code in supplementary materials for the described methodology. |
| Open Datasets | No | The paper states that the data was generated by the authors: “All training, validation, and test programs were generated by treating the Karel DSL as a probabilistic context free grammar and performing top-down expansion with uniform probability at each node.” No link or explicit statement about the public availability of this generated dataset is provided. |
| Dataset Splits | No | The paper mentions the use of “training, validation, and test programs” and states “The dropout, learning rate, and batch size were optimized with grid search for each value of n using a separate set of validation tasks.” However, it does not provide specific split percentages, sample counts, or explicit methodology for how these splits were created or their sizes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. It only mentions that “Training was performed using SGD + momentum and gradient clipping using an in-house toolkit.” |
| Software Dependencies | No | The paper mentions that “Training was performed using SGD + momentum and gradient clipping using an in-house toolkit,” but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific libraries). |
| Experiment Setup | Yes | The input encoder is a 3-layer CNN with a FC+relu layer on top. The output decoder is a 1-layer LSTM. For the META model, the task encoder uses 1-layer CNN to encode the input and output for a single example, which are concatenated on the feature map dimension and fed through a 6-layer CNN with a FC+relu layer on top. Multiple I/O examples were combined with max-pooling on the final vector. All convolutional layers use a 3 3 kernel with a 64-dimensional feature map. The fully-connected and LSTM are 1024-dimensional. Different model sizes are explored later in this section. The dropout, learning rate, and batch size were optimized with grid search for each value of n using a separate set of validation tasks. Training was performed using SGD + momentum and gradient clipping using an in-house toolkit. |