11 citations · 21 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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,…
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors
Einari Vaaras, Manu Airaksinen, Sampsa Vanhatalo +1
The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings.…
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition
Einari Vaaras, Manu Airaksinen, Okko Räsänen
When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and expenses. For case…