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[ViciousSquid] is updating this 90s idea for the 20s with a digital pet squid that uses a neural network to shape its behavior. The neural network that controls the squid’s behavior takes a ...

In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss ...

This repository demonstrates image classification on the CIFAR-10 dataset using a Convolutional Neural Network (CNN) in PyTorch. It walks through important deep learning terms, guides you through ...

Merging with Fourier transforms and pupil aperture scanning causes difficulty in reconstructing high-resolution images by the commonly used deep neural network methods, e.g., based on convolutional ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...

Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D ...

The effectiveness of this approach has led to the development of various neural network architectures, including convolutional neural networks (commonly used in image recognition) and transformer ...

In a study published in Nature Communications, researchers at the University of Wisconsin–Madison introduced a deep learning method capable of automatically identifying transition states in ...