A system that generates images by inducing random fluctuations in a laser beam could slash energy use compared with standard ...

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification ...

This study assesses the performance of CustomNet, a lightweight neural network model trained using NumPy and Pandas, compared to the VGG-16 architecture on the datasets of MNIST, Fashion MNIST, and ...

To overcome this limit, the researchers designed a "photonic multisynapse neural network" that processes information using light in a more direct and physical way.

Additionally, using foundation model encoders directly without fine-tuning resulted in generally poor performance on the classification task. Conclusion: Our findings suggest that deep learning models ...

This repository contains an end-to-end implementation of a convolutional neural network (CNN) trained on the CIFAR-10 dataset for multi-class image classification. It demonstrates fundamental deep ...

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...

However, conventional diagnostic approaches often fail to provide accurate classification in the early stages. This paper proposes a novel approach using advanced computer-aided diagnostic (CAD) ...