Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS)
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Abstract (arXiv)
It is beneficial to develop an efficient machine-learning based method for addition using embedded hexadecimal digits. Through a comparison between human-developed machine learning model and models sampled through Neural Architecture Search (NAS) we determine an efficient approach to solve this problem with a final testing loss of 0.2937 for a human-developed model.
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References · 9
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
- 2019MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalismscited by 662
- 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
- 2020Scaling Laws for Autoregressive Generative Modelingcited by 522
- 2019Analysing Mathematical Reasoning Abilities of Neural Modelscited by 335
- 2019Deep Learning for Symbolic Mathematicscited by 270
- 2021Investigating the Limitations of Transformers with Simple Arithmetic Taskscited by 139
- 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
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