Neural Functional Programming
Authors: John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | 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 feser@csail.mit.edu Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow Microsoft Research {mabrocks,t-algaun,dtarlow}@microsoft.com |
| 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. |