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9 papers · 2 filters
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp +3
Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperatu…
Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation
Raphael C. Brito, Hansenclever F. Bassani
Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously unknown and appear with a given frequency in time series. Another problem strongly relat…
Entropic Out-of-Distribution Detection
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
Out-of-distribution (OOD) detection approaches usually present special requirements (e.g., hyperparameter validation, collection of outlier data) and produce side effects (e.g., cl…
A Semi-Supervised Self-Organizing Map with Adaptive Local Thresholds
Pedro H. M. Braga, Hansenclever F. Bassani
In the recent years, there is a growing interest in semi-supervised learning, since, in many learning tasks, there is a plentiful supply of unlabeled data, but insufficient labeled…
A Semi-Supervised Self-Organizing Map for Clustering and Classification
Pedro H. M. Braga, Hansenclever F. Bassani
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few l…
A Neural Network Architecture for Learning Word-Referent Associations in Multiple Contexts
Hansenclever F. Bassani, Aluizio F. R. Araujo
This article proposes a biologically inspired neurocomputational architecture which learns associations between words and referents in different contexts, considering evidence coll…