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 Programmer-Interpreters
Authors: Scott Reed, Nando de Freitas
ICLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. |
| Researcher Affiliation | Industry | Scott Reed & Nando de Freitas Google Deep Mind London, UK EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Neural programming inference |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | For training data, we used renderings of the 3D car CAD models from (Fidler et al., 2012). |
| Dataset Splits | No | The paper describes training data sizes and testing on unseen data, but does not provide specific details on validation dataset splits by percentages, counts, or references to predefined validation sets. |
| Hardware Specification | No | The paper mentions aspects like 'device memory' and 'LSTM layers' but does not provide specific hardware details such as GPU/CPU models or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions the 'ADAM solver (Kingma & Ba, 2015)' but does not provide specific version numbers for software libraries or frameworks used. |
| Experiment Setup | Yes | We trained the NPI using the ADAM solver (Kingma & Ba, 2015) with base learning rate 0.0001, batch size 1, and decayed the learning rate by a factor of 0.95 every 10,000 steps. |