In 2025 Artprice successfully integrated all the key tools of its proprietary AI (Intuitive Artmarket®) into its internal ...
ncnn-benchmark is a project to test the reasoning performance of neural network using ncnn framework, which includes many common target detection models and tests their reasoning time and accuracy ...
Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
Abstract: Over the past decade, Channel State Information (CSI)-based human activity recognition (HAR) has attracted wide attention. Despite significant advancements, existing CSI-based HAR methods ...
A survey has found that 63.1% of call centre workers experienced customer complaints related to AI systems introduced at their workplaces, with the average number of daily complaints rising from 8.55 ...
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