4 papers
Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation
Einari Vaaras, Manu Airaksinen, Okko Räsänen
Reliable machine-learning models in biomedical settings depend on accurate labels, yet annotating biomedical time-series data remains challenging. Algorithmic sample selection may…
Feature Space Topology Control via Hopkins Loss
Einari Vaaras, Manu Airaksinen
Feature space topology refers to the organization of samples within the feature space. Modifying this topology can be beneficial in machine learning applications, including dimensi…
Investigating Affect Mining Techniques for Annotation Sample Selection in the Creation of Finnish Affective Speech Corpus
Kalle Lahtinen, Einari Vaaras, Liisa Mustanoja +1
Study of affect in speech requires suitable data, as emotional expression and perception vary across languages. Until now, no corpus has existed for natural expression of affect in…
PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
Einari Vaaras, Manu Airaksinen, Okko Räsänen
Self-supervised learning (SSL) is a data-driven learning approach that utilizes the innate structure of the data to guide the learning process. In contrast to supervised learning,…