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A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab. Of course I mean to say that Hicham Hallak won the… | 19 comments on LinkedIn
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
#technical#GraphAI
·linkedin.com·
A Graph Neural Network (GNN) won the highly competitive Causal Discovery competition arranged by ADIA Lab
MLX-graphs — mlx-graphs 0.0.3 documentation
MLX-graphs — mlx-graphs 0.0.3 documentation
Apple presented MLX-graphs: the GNN library for the MLX framework specifically optimized for Apple Silicon. Since the CPU/GPU memory is shared on M1/M2/M3, you don’t have to worry about moving tensors around and at the same time you can enjoy massive GPU memory of latest M2/M3 chips (64 GB MBPs and MacMinis are still much cheaper than A100 80 GB). For starters, MLX-graphs includes GCN, GAT, GIN, GraphSAGE, and MPNN models and a few standard datasets.
#GraphAI#open source#technical#research
·mlx-graphs.github.io·
MLX-graphs — mlx-graphs 0.0.3 documentation