New methods for metastimuli: architecture, embeddings, and neural network optimization
This work addresses methodological improvements for a niche domain of human-computer interaction, but it appears incremental as it builds on a previously presented architecture without demonstrating broad impact.
The authors tackled the problem of optimizing the 'metastimuli architecture' for human learning via machine learning in personal information management systems, presenting results from neural network training and parameter optimization to enhance system performance.
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.