Improving the Robustness of Neural Multiplication Units with Reversible Stochasticity Article Swipe
Bhumika Mistry
,
Katayoun Farrahi
,
Jonathon Hare
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.05624
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.05624
Multilayer Perceptrons struggle to learn certain simple arithmetic tasks. Specialist neural modules for arithmetic can outperform classical architectures with gains in extrapolation, interpretability and convergence speeds, but are highly sensitive to the training range. In this paper, we show that Neural Multiplication Units (NMUs) are unable to reliably learn tasks as simple as multiplying two inputs when given different training ranges. Causes of failure are linked to inductive and input biases which encourage convergence to solutions in undesirable optima. A solution, the stochastic NMU (sNMU), is proposed to apply reversible stochasticity, encouraging avoidance of such optima whilst converging to the true solution. Empirically, we show that stochasticity provides improved robustness with the potential to improve learned representations of upstream networks for numerical and image tasks.
Related Topics
Concepts
Interpretability
Robustness (evolution)
Perceptron
Extrapolation
Artificial neural network
Computer science
Multiplication (music)
Artificial intelligence
Range (aeronautics)
Convergence (economics)
Machine learning
Mathematical optimization
Mathematics
Statistics
Engineering
Economic growth
Aerospace engineering
Combinatorics
Chemistry
Biochemistry
Economics
Gene
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.05624
- https://arxiv.org/pdf/2211.05624
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308830586
All OpenAlex metadata
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https://openalex.org/W4308830586Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.05624Digital Object Identifier
- Title
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Improving the Robustness of Neural Multiplication Units with Reversible StochasticityWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-10Full publication date if available
- Authors
-
Bhumika Mistry, Katayoun Farrahi, Jonathon HareList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.05624Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.05624Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2211.05624Direct OA link when available
- Concepts
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Interpretability, Robustness (evolution), Perceptron, Extrapolation, Artificial neural network, Computer science, Multiplication (music), Artificial intelligence, Range (aeronautics), Convergence (economics), Machine learning, Mathematical optimization, Mathematics, Statistics, Engineering, Economic growth, Aerospace engineering, Combinatorics, Chemistry, Biochemistry, Economics, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.networks | 119 |
| abstract_inverted_index.proposed | 86 |
| abstract_inverted_index.provides | 107 |
| abstract_inverted_index.reliably | 47 |
| abstract_inverted_index.struggle | 2 |
| abstract_inverted_index.training | 32, 59 |
| abstract_inverted_index.upstream | 118 |
| abstract_inverted_index.avoidance | 92 |
| abstract_inverted_index.classical | 16 |
| abstract_inverted_index.different | 58 |
| abstract_inverted_index.encourage | 72 |
| abstract_inverted_index.inductive | 67 |
| abstract_inverted_index.numerical | 121 |
| abstract_inverted_index.potential | 112 |
| abstract_inverted_index.sensitive | 29 |
| abstract_inverted_index.solution, | 80 |
| abstract_inverted_index.solution. | 101 |
| abstract_inverted_index.solutions | 75 |
| abstract_inverted_index.Multilayer | 0 |
| abstract_inverted_index.Specialist | 9 |
| abstract_inverted_index.arithmetic | 7, 13 |
| abstract_inverted_index.converging | 97 |
| abstract_inverted_index.outperform | 15 |
| abstract_inverted_index.reversible | 89 |
| abstract_inverted_index.robustness | 109 |
| abstract_inverted_index.stochastic | 82 |
| abstract_inverted_index.Perceptrons | 1 |
| abstract_inverted_index.convergence | 24, 73 |
| abstract_inverted_index.encouraging | 91 |
| abstract_inverted_index.multiplying | 53 |
| abstract_inverted_index.undesirable | 77 |
| abstract_inverted_index.Empirically, | 102 |
| abstract_inverted_index.architectures | 17 |
| abstract_inverted_index.stochasticity | 106 |
| abstract_inverted_index.Multiplication | 41 |
| abstract_inverted_index.extrapolation, | 21 |
| abstract_inverted_index.stochasticity, | 90 |
| abstract_inverted_index.representations | 116 |
| abstract_inverted_index.interpretability | 22 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |