The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data ...

A collaborative research team led by Professor Pan Feng from the School of New Materials at Peking University Shenzhen Graduate School has developed a topology-based variational autoencoder framework ...

Unlike traditional databases, knowledge graphs organize information as nodes and edges, making them better for AI systems that reason & infer.

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Graph databases and knowledge graphs—especially when combined—fulfill this role. This is part of a rapidly unfolding movement in which “databases have evolved from merely storage layers for ...

Learn how large language models like ChatGPT make knowledge graph creation accessible, revealing hidden connections in your data.

Integrating adversarial training into the graph autoencoder (GAE) framework imposes regularization on the latent space, improving the distinction between normal and abnormal data representations. The ...