Neural Arithmetic Units
Authors: Andreas Madsen, Alexander Rosenberg Johansen
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTAL RESULTS |
| Researcher Affiliation | Academia | Andreas Madsen Computationally Demanding amwebdk@gmail.com Alexander Rosenberg Johansen Technical University of Denmark aler@dtu.dk |
| Pseudocode | Yes | Algorithm 1 deļ¬nes the exact procedure to generate the data, where an interpolation range will be used for training and validation and an extrapolation range will be used for testing. |
| Open Source Code | Yes | 1Implementation is available on Git Hub: https://github.com/Andreas Madsen/stable-nalu. |
| Open Datasets | Yes | Furthermore, we improve upon existing benchmarks in Trask et al. (2018) by expanding the simple function task , expanding MNIST Counting and Arithmetic Tasks with a multiplicative task, and using an improved success-criterion Madsen & Johansen (2019). |
| Dataset Splits | Yes | Each experiment is trained for 5 106 iterations with early stopping by using the validation dataset, which is based on the interpolation range (details in Appendix C.2). |
| Hardware Specification | Yes | Training takes about 8 hours on a single CPU core(8-Core Intel Xeon E5-2665 2.4GHz). |
| Software Dependencies | No | The paper mentions 'Adam optimization (Kingma & Ba, 2014)' and refers to 'pytorch' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Each experiment is trained for 5 106 iterations with early stopping by using the validation dataset, which is based on the interpolation range (details in Appendix C.2). |