The final output layer uses softmax activation to handle multi-class classification. → The model is compiled using the Adam optimizer (adaptive learning rate) and the sparse_categorical_crossentropy ...

However, Transformers’ intensive and complicated computations, especially the crucial nonlinear Softmax and activation (Act) functions, pose challenges for training deployment on edge. This brief ...

which converts classical time-series information to graph representations. We cast the problem as an emotion classification task, enabling the proposed model to learn associations between the labels ...

Just as with the first three editions, the new edition walks the reader through the classic parts of combinatorial enumeration and graph theory ... enumeration under group action, generating functions ...

Implementation of "Just Balance GNN" for graph classification and node clustering from the paper "Simplifying Clusterings with Graph Neural Networks".

To overcome this gap, we propose a novel Cross-Modal Pathological Organ Diagnosis Model that integrates tongue images and textual descriptions for more accurate pathological classification Methods: ...

Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano and IUNET, piazza L. da Vinci 32, 20133, Milano, Italy ...

Department of Computer Science, University of Bath, Bath BA2 7AY, U.K.