Agentic Heuristic Learning Studio for executable human activity recognition proposes learning from examples and rules instead of backpropagation
Read the original at arxiv.org→arXiv:2609.16065v1 Announce Type: new Abstract: Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a complementary view...
Original headline: "You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition"
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- Sep 16, 04:00 UTC arXiv cs.LG lead source You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition