Machine learning to analyze single-case data : a proof of concept

Visual analysis is the most commonly used method for interpreting data from singlecase designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach

Survey on activation functions for optical neural networks

ABSTRACT: Integrated photonics arises as a fast and energy-efficient technology for the implementation of artificial neural networks (ANNs). Indeed, with the growing interest in ANNs, photonics shows great promise to overcome current limitations of electronic-based implementation. For example, it has been shown that neural networks integrating optical matrix multiplications can potentially run two orders of magnitude faster than their electronic counterparts. However, the transposition in the optical domain of the activation functions, which is a key feature of ANNs, remains a challenge. There is no direct optical implementation of state-of-the-art activation functions. Currently, most designs require time-consuming and power-hungry electro-optical conversions. In this survey, we review both all-optical and opto-electronic activation functions proposed in the state-of-the-art. We present activation functions with their key characteristics, and we summarize challenges for their use in the context of all-optical neural networks. We then highlight research directions for the implementation of fully optical neural networks