12 citations · 20 across the 5 of their papers we have counts for
5 papers
Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scale
Jonas Oppenlaender, Joonas Hämäläinen
Large language models (LLMs) are increasingly used for analytical tasks, yet their effectiveness in real-world applications remains underexamined, partly due to the opacity of prop…
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…
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…
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…