AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Synthesizing realistic graphs at scale is vital when the graphs of interest are large and real-world samples are limited or access-sensitive. Diffusion-based generators have recently driven much of the progress, offering high modeling capacity, but most such methods have quadratic computational complexity and are hence...
This study introduces an approach leveraging deep neural networks to identify high-level, abstract concepts in visual user-generated content (UGC) for restaurants, and proposes two weak labeling methods: one utilizing existing restaurant-quality signals, and the other extracting relevant labels from review texts via qu...
This paper introduces a novel prototype-enhanced hypergraph learning approach for node classification in HINs using hypergraphs instead of graphs, which captures higher-order relationships among nodes and extracts semantic information without relying on metapaths.
This work presents an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes.
MMArt is introduced, a large-scale dataset of 74,234 WikiArt paintings, each annotated with four independently annotated perspectives plus a harmonized unified caption, produced by specialized vision-language models or human annotation and validated through complementary quality evaluations.
This survey focuses on four fundamental data-centric challenges: (1) Incompleteness, real-world graphs have missing nodes, edges, (2) Missing nodes, edges, and (3) Missing edges.
This paper proposes Scenemash, a system that generates multimodal summaries of multiple alternative routes between locations in a city by jointly analysing distributions of a large number of semantic concepts detected in the visual content and latent topics extracted from associated text.
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