Bridging the gap between theory and reality, material testing transforms dense technical specifications into the physical ...
Abstract: Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, ...
Abstract: A graph structure is a powerful mathematical abstraction, which can not only represent information about individuals but also capture the interactions between individuals for reasoning.
Current artificial vision systems suffer from high energy consumption and latency due to the von Neumann bottleneck and separated spatiotemporal processing. Wu et al. propose a vision system achieving ...
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