Programming with a Differentiable Forth Interpreter
Authors: Matko Bošnjak, Tim Rocktäschel, Jason Naradowsky, Sebastian Riedel
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. We evaluate @4 on three tasks. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University College London, London, UK 2Department of Computer Science, University of Oxford, Oxford, UK 3Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK. |
| Pseudocode | Yes | Listing 1: Three code alternatives (white lines are common to all, coloured/lettered lines are alternative-specific): i) Bubble sort in Forth (a lines green), ii) PERMUTE sketch (b lines blue), and iii) COMPARE sketch (c lines yellow). Listing 2: Manipulate sketch (a lines green) and the choose sketch (b lines blue) for Elementary Addition. Listing 3: Core of the Word Algebra Problem sketch. |
| Open Source Code | No | The paper states that TensorFlow is "Software available from tensorflow.org", but does not provide any statement or link indicating that their own implementation code (@4 or experimental code) is open-source or publicly available. |
| Open Datasets | Yes | We evaluate the model on the Common Core (CC) dataset, introduced by Roy & Roth (2015). |
| Dataset Splits | No | The paper mentions varying training and test set lengths (e.g., "For a given test sequence length, we vary the training set lengths..."), but does not provide specific details on train/validation/test dataset splits, only implicitly discussing training and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "TensorFlow (Abadi et al., 2015) implementation" but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | No | The paper states, "Full details of the experimental setup can be found in Appendix E." However, Appendix E is not provided in the given text, and the main body does not contain specific hyperparameters or system-level training settings. |