This is a Python implementation of a simple feedforward neural network to recognize handwritten digits from the MNIST dataset. It trains on the provided MNIST training set and evaluates its ...

This is a small project about neural networks and backpropagation that I did in school. It uses the MNIST database to learn handwriting recognition. The whole neural network was built from scratch in ...

Artificial Neural Networks (ANNs) have their roots established in the inspiration ... including CIFAR-10 and Fashion-MNIST. Their accuracy and performance consistently matched or exceeded that of the ...

Spiking neural networks (SNNs) are a subset of ANNs that incorporate additional ... These spatial and temporal motifs are first generated from the pre-training of spatial (e.g., MNIST) and temporal (e ...

This insight provides an alternative perspective on the unexpectedly good generalization even of highly over-parameterized neural networks. We substantiate our theoretical findings through experiments ...

In this paper, an optical convolutional neural network, combining a novel architectural design ... to “9” in Modified National Institute of Standards and Technology (MNIST) database, achieving an ...

One of the most agonizing experiences a cancer patient suffers is waiting without knowing: waiting for a diagnosis, waiting ...

Neural networks have revolutionized the fields of artificial intelligence (AI) and machine learning by providing a flexible, and scalable, means to solve complex, and traditionall ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

The authors develop an analysis package for characterizing the activity of neural dendrites and soma ... suggestive of network-level computational mechanisms.