Efficient Design of Neural Networks with Random Weights

TL;DR

This paper presents a method to significantly reduce the number of hidden units in random-weight neural networks, maintaining accuracy while decreasing computational costs.

Contribution

The authors introduce primary and secondary hidden units, enabling at least tenfold reduction in hidden units without major accuracy loss.

Findings

At least 10x reduction in hidden units achieved.

Significant decrease in inference computation time.

Minor impact on network accuracy.

Abstract

Single layer feedforward networks with random weights are known for their non-iterative and fast training algorithms and are successful in a variety of classification and regression problems. A major drawback of these networks is that they require a large number of hidden units. In this paper, we propose a technique to reduce the number of hidden units substantially without affecting the accuracy of the networks significantly. We introduce the concept of primary and secondary hidden units. The weights for the primary hidden units are chosen randomly while the secondary hidden units are derived using pairwise combinations of the primary hidden units. Using this technique, we show that the number of hidden units can be reduced by at least one order of magnitude. We experimentally show that this technique leads to significant drop in computations at inference time and has only a minor impact on network accuracy. A huge reduction in computations is possible if slightly lower accuracy is acceptable.