See how DenseNets reuse features across layers, reduce parameters, and improve training efficiency.

This study focuses on comparing the performance of K-Nearest Neighbors (KNN), TabNet, and Wide & deep learning methods in classifying Distributed Denial of Service (DDoS) attacks on Software-Defined ...

K-Nearest Neighbors (KNN) algorithm, its application in crack analysis for concrete structures, and how it can be utilized for crack detection along with estimating the length of the crack. A ...

Recently, Shaila Niazi, a third-year doctoral student in Çamsari’s lab, achieved a significant breakthrough in that effort, becoming the first to use probabilistic hardware to train a deep generative ...

Geoffrey Hinton, professor at the University of Toronto and engineering fellow at Google Brain, recently published a paper on the Forward-Forward algorithm (FF), a technique for training neural ...

To compare the performance of deep learning model to that of other models based on state-of-the-art machine learning algorithms, the same datasets were used to develop a k nearest neighbor (KNN) model ...

A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make ...