The present study introduces a novel approach employing weightless neural networks
(WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN …

The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop
AI systems with more human-like reasoning capabilities by combining symbolic reasoning …

Open set recognition is a classification-like task. It is accomplished not only by the
identification of observations which belong to targeted classes (ie, the classes among those …

Weightless neural networks (WNNs) are a class of machine learning model which use table
lookups to perform inference, rather than the multiply-accumulate operations typical of deep …

WiSARD belongs to the class of weightless neural networks, and it is based on a neural
model which uses lookup tables to store the function computed by each neuron rather than …

Training part-of-speech taggers (POS-taggers) requires iterative time-consuming
convergence-dependable steps, which involve either expectation maximization or weight …

Abstract This work examines Fault Detection and Diagnosis (FDD) based on Weightless
Neural Networks (WNN) with applications in univariate and multivariate dynamic systems …

''Extreme edge” devices, such as smart sensors, are a uniquely challenging environment for
the deployment of machine learning. The tiny energy budgets of these devices lie beyond …

Credit analysis is a real-world classification problem where it is quite common to find
datasets with a large amount of noisy data. State-of-the-art classifiers that employ error …

Conditional branch prediction allows the speculative fetching and execution of instructions
before knowing the direction of conditional statements. As in other areas, machine learning …