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 ...

The classification problem represents a funda-mental challenge in machine learning, with logistic regression serving as a traditional yet widely utilized method across various scientific disciplines.

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 ...

Article citations More>> Khvostikov, A., Aderghal, K., Benois-Pineau, J., Krylov, A. and Catheline, G. (2018) 3D CNN-Based Classification Using sMRI and MD-DTI Images for Alzheimer Disease Studies.