We previously introduced a “range corrected” Δ−machine learning potential (ΔMLP) that used deep neural networks to improve the accuracy of combined quantum mechanical/molecular mechanical (QM/MM) ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher ...

This project involves implementing the forward pass of an 18-layer Convolutional Neural Network (CNN) in MATLAB for object detection. The goal is to classify 32x32x3 images into one of ten categories, ...

Neural Networks show the results in form of "Alarm" and "Warning". The intervals for each sensor in each communication form are shown in the figures below. The Neural Networks are already adjusted and ...

This brief discusses the output synchronization problem for coupled neural networks with multiple delayed output couplings (CNNMDOCs). On one hand, by utilizing adaptive state feedback controller and ...

We have developed a neural network with fast and slow dynamics, which are inspired by the hierarchy of timescales on neural activities in the cortex. The slow dynamics store the history of inputs and ...

Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling ...