New methods for metastimuli: architecture, embeddings, and neural network optimization

TL;DR

This paper introduces six new methodological advancements for the metastimuli architecture, enhancing human learning via machine learning of spatially correlated structural data within personal information systems, including neural network and embedding innovations.

Contribution

It presents novel architectural, embedding, and optimization techniques for the metastimuli system, including a new 'nabla' embedding inspired by linguistics, and explores hyper- and meta-parameter tuning.

Findings

Effective neural network application for metastimuli

Successful atom categorization in PIMS

Optimization techniques improve system performance

Abstract

Six significant new methodological developments of the previously-presented "metastimuli architecture" for human learning through machine learning of spatially correlated structural position within a user's personal information management system (PIMS), providing the basis for haptic metastimuli, are presented. These include architectural innovation, recurrent (RNN) artificial neural network (ANN) application, a variety of atom embedding techniques (including a novel technique we call "nabla" embedding inspired by linguistics), ANN hyper-parameter (one that affects the network but is not trained, e.g. the learning rate) optimization, and meta-parameter (one that determines the system performance but is not trained and not a hyper-parameter, e.g. the atom embedding technique) optimization for exploring the large design space. A technique for using the system for automatic atom categorization in a user's PIMS is outlined. ANN training and hyper- and meta-parameter optimization results are presented and discussed in service of methodological recommendations.