D-HTM adds a shared associative memory to hierarchical temporal memory so that precursor signatures learned on one entity can trigger preemptive warnings on related entities, yielding an average 8.1-sample lead time on tested datasets.
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Soft-labelling ordinal deep learning with binomial, beta, triangular, and exponential distributions improves KL and CPPD grading over one-hot baselines on knee X-rays.
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Distributed Hierarchical Temporal Memory with Shared Associative Memory for Cross-Entity Preemptive Warning
D-HTM adds a shared associative memory to hierarchical temporal memory so that precursor signatures learned on one entity can trigger preemptive warnings on related entities, yielding an average 8.1-sample lead time on tested datasets.
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From Kellgren-Lawrence to Calcium Pyrophosphate Crystal Deposition: A Soft-Labelling Framework for Knee Osteoarthritis Assessmen
Soft-labelling ordinal deep learning with binomial, beta, triangular, and exponential distributions improves KL and CPPD grading over one-hot baselines on knee X-rays.