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 Functional Programming
Authors: John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that this language allows to learn far more programs than existing baselines. |
| Researcher Affiliation | Collaboration | John K. Feser Massachusetts Institute of Technology EMAIL Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow Microsoft Research EMAIL |
| Pseudocode | Yes | function FOLDLI(list, acc, func) idx 0 for ele in list do acc func(acc, ele, idx) idx idx + 1 return acc |
| Open Source Code | No | We aim to release Terpre T, together with these models, under an open source license in the near future. |
| Open Datasets | No | The paper mentions generating input/output pairs for tasks ("For all tasks, three groups of five input/output example pairs were sampled as training data"), but it does not specify a publicly available dataset by name, citation, or link that can be accessed by others. |
| Dataset Splits | No | The paper specifies training and test data splits ("three groups of five input/output example pairs were sampled as training data and another 25 input/output pairs as test data") but does not mention a separate validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | All of our models are implemented in Terpre T (Gaunt et al., 2016b) and we learn using Terpre T s TENSORFLOW (Abadi et al., 2015) backend. Software names (Terpre T, TensorFlow) are mentioned, but specific version numbers are not provided. |
| Experiment Setup | Yes | After training for 3500 epochs (tests with longer training runs showed no significant changes in the outcomes)... We ran the remaining experiments with the best configuration obtained by this process: the RMSProp optimization algorithm, a learning rate of 0.1, clipped gradients at 1, and no gradient noise. |