Improving the Robustness of Neural Multiplication Units with Reversible Stochasticity

• URL: http://arxiv.org/abs/2211.05624v1
• Date: Thu, 10 Nov 2022 14:56:37 GMT
• Title: Improving the Robustness of Neural Multiplication Units with Reversible Stochasticity
• Authors: Bhumika Mistry, Katayoun Farrahi, Jonathon Hare
• Abstract summary: Multilayer Perceptrons struggle to learn certain simple arithmetic tasks. Specialist neural NMU (sNMU) is proposed to apply reversibleity, encouraging avoidance of such optima.
• Score: 2.4278445972594525
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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