Metalearning: Sparse Variable-Structure Automata

TL;DR

This paper introduces a metalearning approach using actor-critic algorithms to dynamically select the optimal dimension for sparse coding in autoencoders, balancing accuracy and computational complexity.

Contribution

It presents a novel online method that adaptively determines the number of basis vectors for sparse coding using reinforcement learning techniques.

Findings

Effective online control of representation dimension.

Reduced computational complexity with maintained accuracy.

Adaptive sparse coding improves autoencoder performance.

Abstract

Dimension of the encoder output (i.e., the code layer) in an autoencoder is a key hyper-parameter for representing the input data in a proper space. This dimension must be carefully selected in order to guarantee the desired reconstruction accuracy. Although overcomplete representation can address this dimension issue, the computational complexity will increase with dimension. Inspired by non-parametric methods, here, we propose a metalearning approach to increase the number of basis vectors used in dynamic sparse coding on the fly. An actor-critic algorithm is deployed to automatically choose an appropriate dimension for feature vectors regarding the required level of accuracy. The proposed method benefits from online dictionary learning and fast iterative shrinkage-thresholding algorithm (FISTA) as the optimizer in the inference phase. It aims at choosing the minimum number of bases for the overcomplete representation regarding the reconstruction error threshold. This method allows for online controlling of both the representation dimension and the reconstruction error in a dynamic framework.