Event-level MAE embeddings plus UMAP/HDBSCAN or K-Means clustering recover 15 hydroacoustic classes from multi-year Mayotte data with ~1 hour of annotation and detector-comparable F1.
In: 2019 International Conference on Document Analysis and Recognition (ICDAR)
2 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
2
Pith papers citing it
19
external citations · external index
years
2026 2representative citing papers
Kinematic handwriting features from the sigma-lognormal model predict children's grade, gender, and academic performance on a large Japanese student dataset using regression and random forest models.
citing papers explorer
-
A Self-Supervised Approach for Minimal-Annotation Hydroacoustic Data Exploration
Event-level MAE embeddings plus UMAP/HDBSCAN or K-Means clustering recover 15 hydroacoustic classes from multi-year Mayotte data with ~1 hour of annotation and detector-comparable F1.
-
Prediction of Grade, Gender, and Academic Performance of Children and Teenagers from Handwriting Using the Sigma-Lognormal Model
Kinematic handwriting features from the sigma-lognormal model predict children's grade, gender, and academic performance on a large Japanese student dataset using regression and random forest models.