Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
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BlazingAML uses a multi-stage graph mining framework and compiler to express fuzzy AML patterns, matching SOTA F1 scores while delivering 210x CPU and 333x GPU speedups on IBM datasets.
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Graph Retention Networks for Dynamic Graphs
Graph Retention Networks extend retention to dynamic graphs to enable parallelizable training, O(1) inference, and chunkwise long-term training while delivering competitive performance with major efficiency gains.
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BlazingAML: High-Throughput Anti-Money Laundering (AML) via Multi-Stage Graph Mining
BlazingAML uses a multi-stage graph mining framework and compiler to express fuzzy AML patterns, matching SOTA F1 scores while delivering 210x CPU and 333x GPU speedups on IBM datasets.