Classification fields are infinite recursive hierarchical cluster structures generated by a local refinement rule, and a ReLU network predictor learned from finite prefixes can approximate the generator and extend it to deeper levels with exponential convergence in the completed cell metric.
Hierarchical clustering schemes.Psychometrika, 32(3):241–254
2 Pith papers cite this work. Polarity classification is still indexing.
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ROAST selectively trains anomaly detectors on less vulnerable patient data with targeted outlier exposure, boosting recall by 16.2% in black-box settings and reducing training time by 88.3%.
citing papers explorer
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Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examples
Classification fields are infinite recursive hierarchical cluster structures generated by a local refinement rule, and a ReLU network predictor learned from finite prefixes can approximate the generator and extend it to deeper levels with exponential convergence in the completed cell metric.
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ROAST: Risk-aware Outlier-exposure for Adversarial Selective Training of Anomaly Detectors Against Evasion Attacks
ROAST selectively trains anomaly detectors on less vulnerable patient data with targeted outlier exposure, boosting recall by 16.2% in black-box settings and reducing training time by 88.3%.