AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Recent topic models leverage pretrained embeddings, but neural architectures produce latent representations without grounding in specific texts, and clustering-based pipelines assign representative documents only post hoc, relying on absolute distances distorted by hubness and anisotropy in high-dimensional spaces. We...
Content-based image retrieval (CBIR) has evolved significantly with the advent of deep learning models, yet effectively ranking similar images remains a challenging task, particularly in high-dimensional feature spaces where pairwise distance measures often fail to capture complex contextual relationships and the seman...
GRaCE surpasses RaDE and Original Features across diverse datasets, including textual and image collections, excelling in retrieval, classification, and clustering tasks, considering state-of-the-art Transformer models as feature descriptors and Graph Convolutional Networks models in classification tasks.
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