26 citations · 43 across the 11 of their papers we have counts for
11 papers
Representation Finetuning for Continual Learning
Haihua Luo, Xuming Ran, Tommi Kärkkäinen +4
The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. While pre-trained models have shown powerful performance in co…
Dementia classification from spontaneous speech using wrapper-based feature selection
Marko Niemelä, Mikaela von Bonsdorff, Sami Äyrämö +1
Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activities. As populations globally age, nearl…
The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance
Mika Setälä, Ville Heilala, Pieta Sikström +1
This study investigated the students' perceptions of using Generative Artificial Intelligence (GenAI) in upper-secondary mathematics education. Data was collected from Finnish high…
Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model
Marko Niemelä, Mikaela von Bonsdorff, Sami Äyrämö +1
Memory disorders are a central factor in the decline of functioning and daily activities in elderly individuals. The confirmation of the illness, initiation of medication to slow i…
Minimal Learning Machine for Multi-Label Learning
Joonas Hämäläinen, Antoine Hubermont, Amauri Souza +3
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this pa…
Knowledge Discovery from Atomic Structures using Feature Importances
Joakim Linja, Joonas Hämäläinen, Antti Pihlajamäki +4
Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications. This need goes…
An Additive Autoencoder for Dimension Estimation
Tommi Kärkkäinen, Jan Hänninen
An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed…
Scalable Initialization Methods for Large-Scale Clustering
Joonas Hämäläinen, Tommi Kärkkäinen, Tuomo Rossi
In this work, two new initialization methods for K-means clustering are proposed. Both proposals are based on applying a divide-and-conquer approach for the K-means|| type of an in…
Mislabel Detection of Finnish Publication Ranks
Anton Akusok, Mirka Saarela, Tommi Kärkkäinen +2
The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM). The purpose is to introduce and test recently propos…
Software Framework for Tribotronic Systems
Jarno Kansanaho, Tommi Kärkkäinen
Increasing the capabilities of sensors and computer algorithms produces a need for structural support that would solve recurring problems. Autonomous tribotronic systems self-regul…
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
Joonas Hämäläinen, Alisson S. C. Alencar, Tommi Kärkkäinen +3
The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, whe…