Neural Programmer-Interpreters
Authors: Scott Reed, Nando de Freitas
ICLR 2016 | Conference PDF | Archive PDF | Plain Text | 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 scott.ellison.reed@gmail.com nandodefreitas@google.com |
| 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. |