arXiv:2608. 11064v1 Announce Type: cross Abstract: Artificial intelligence (AI) has become a powerful approach to solving complex problems in critical domains.
By Ali Saleh, Abdul Karim Gizzini, Mohamad Ghassany, Ali J. Ghandour
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.
arXiv:2411. 05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification.
By Antonio De Santis, Riccardo Campi, Matteo Bianchi, Marco Brambilla
arXiv:2606. 00557v1 Announce Type: new Abstract: To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also examine its internal inference mechanisms to capture the complete picture.
By Ping Xiong, Thomas Schnake, Gr\'egoire Montavon, Klaus-Robert M\"uller, Shinichi Nakajima
arXiv:2606. 30226v1 Announce Type: new Abstract: Hessian spectral properties are a standard tool in analysing neural-network training, with eigenvalues linked to sharpness, generalization, and optimization dynamics.
By Marcelina Marjankowska, Valerio Modugno, Paolo Barucca
arXiv:2606. 04583v1 Announce Type: new Abstract: Many researchers investigated neural networks with some of their weights fixed to values randomly drawn from a given distribution, e.
By Ethem Alpaydin