The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data ...

These results highlight the potential of the Autoencoder–GCN pipeline as a scalable and reliable solution for AMS structure recognition under real-world constraints.

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

Article citations More>> Shevitski, B., Watkins, Y., Man, N. and Girard, M. (2023) Digital Signal Processing Using Deep Neural Networks: Evaluating the Effectiveness of Hybrid Autoencoder/ Transformer ...

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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 ...

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1 Introduction The integration of neural network models in autonomous robotics represents a monumental leap in artificial intelligence and robotics. These models, mirroring the human brain's ...

ABSTRACT: Convolutional auto-encoders have shown their remarkable performance in stacking deep convolutional neural networks for classifying image data during the past several years. However, they are ...

Researchers combine acoustic monitoring with a neural network to identify fish activity on coral reefs by sound. They trained the network to sort through the deluge of acoustic data automatically ...