Learning Division with Neural Arithmetic Logic Modules Article Swipe

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Bhumika Mistry , Katayoun Farrahi , Jonathon Hare ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2110.05177

To achieve systematic generalisation, it first makes sense to master simple tasks such as arithmetic. Of the four fundamental arithmetic operations (+,-,$\times$,$÷$), division is considered the most difficult for both humans and computers. In this paper we show that robustly learning division in a systematic manner remains a challenge even at the simplest level of dividing two numbers. We propose two novel approaches for division which we call the Neural Reciprocal Unit (NRU) and the Neural Multiplicative Reciprocal Unit (NMRU), and present improvements for an existing division module, the Real Neural Power Unit (Real NPU). Experiments in learning division with input redundancy on 225 different training sets, find that our proposed modifications to the Real NPU obtains an average success of 85.3$\%$ improving over the original by 15.1$\%$. In light of the suggestion above, our NMRU approach can further improve the success to 91.6$\%$.

Related Topics

Division (Mathematics)

Arithmetic

Computer Science

Mathematics

Artificial Intelligence

Concepts

Division (mathematics) Arithmetic Computer science Mathematics Artificial intelligence Algebra over a field Pure mathematics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2110.05177
PDF: https://arxiv.org/pdf/2110.05177
OA Status: green
Cited By: 1
References: 12
Related Works: 10
OpenAlex ID: https://openalex.org/W3205950948

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DOI: https://doi.org/10.48550/arxiv.2110.05177

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Title: Learning Division with Neural Arithmetic Logic Modules

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-10-11

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Authors: Bhumika Mistry, Katayoun Farrahi, Jonathon Hare

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Landing page: https://arxiv.org/abs/2110.05177

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PDF URL: https://arxiv.org/pdf/2110.05177

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Concepts: Division (mathematics), Arithmetic, Computer science, Mathematics, Artificial intelligence, Algebra over a field, Pure mathematics

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Cited by: 1

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