Compact Neural Network: Parameter Reduction using Sign Combinations

Chik, David (2014) Compact Neural Network: Parameter Reduction using Sign Combinations. ICIC Express Letters, 8 (8). ISSN 1881-803X

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One problem of using neural network to learn real life data is that there are too many parameters (weights) to tune. Here, a new method of reducing the number of parameters is proposed. The basic idea is to represent individual nodes by vectors of different combinations of positive or negative signs. Nonlinear relationship between the nodes can therefore be encoded in their sign combinations. This Compact Neural Network can implement Parity without hidden nodes. It can also achieve the same generalization performance on real life data using a fewer number of hidden layers and nodes. More importantly, this network does not need heuristics (e.g. manually design a polynomial form as in functional link neural network) and can be scaled up easily to become a deep network for solving very complicated classification problems.

Item Type: Journal Article
Keywords: Neural networks, parameter reduction, vector, classification
Faculty: ARCHIVED Faculty of Science & Technology (until September 2018)
Depositing User: Dr Antesar Shabut
Date Deposited: 12 Oct 2016 10:44
Last Modified: 09 Sep 2021 16:15

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