EurekAlert!10d
D2-GCN: a graph convolutional network with dynamic disentanglement for node classification
GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...
Armed robbery in Revesby9d
D2-GCN: Dynamic Graph Network for Node Classification
Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different ...
DLSS 4 is incredible, but I’m still worried about its future
Diving deeper into DLSS 4 reveals just how impressive it is -- and just how problematic it could be in the future.
IEEE17d
A Sparse Cross Attention-Based Graph Convolution Network With Auxiliary Information Awareness for Traffic Flow Prediction
Abstract: Deep graph convolutional networks (GCNs) have shown promising performance in ... Second, the computational complexity of dynamic graph convolution operations grows quadratically with the ...
Nvidia RTX 5090 review: fast, but not nearly twice as fast
The RTX 5090 is the most insane graphics card Nvidia has ever released, and that's exactly why it's so impressive.
Interesting Engineering on MSN6d
The early minds behind the machine: Founders of artificial intelligence
Turing's 1950 paper didn't just pose the profound question, "Can machines think?". It ignited a quest to build AI technology ...
GitHub15d
Traffic Prediction Benchmark
Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Depeng Jin and Yong Li. Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution. Figure 1. The architecture of DGCRN.
The New York Times9d
What's Going On in This Graph?
What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...