19 citations · 39 across the 16 of their papers we have counts for
6 papers · 1 filter
An XAI Social Media Platform for Teaching K-12 Students AI-Driven Profiling, Clustering, and Engagement-Based Recommending
Nicolas Pope, Juho Kahila, Henriikka Vartiainen +5
This paper, submitted to the special track on resources for teaching AI in K-12, presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for stud…
Regional Ocean Forecasting with Hierarchical Graph Neural Networks
Daniel Holmberg, Emanuela Clementi, Teemu Roos
Accurate ocean forecasting systems are vital for understanding marine dynamics, which play a crucial role in environmental management and climate adaptation strategies. Traditional…
LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search
Elias Jääsaari, Ville Hyvönen, Teemu Roos
Approximate nearest neighbor (ANN) search is a key component in many modern machine learning pipelines; recent use cases include retrieval-augmented generation (RAG) and vector dat…
Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
Tomi Silander, Janne Leppä-aho, Elias Jääsaari +1
We introduce an information theoretic criterion for Bayesian network structure learning which we call quotient normalized maximum likelihood (qNML). In contrast to the closely rela…
An Educational Tool for Learning about Social Media Tracking, Profiling, and Recommendation
Nicolas Pope, Juho Kahila, Jari Laru +3
This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms - tracking, profiling, and content rec…
Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks
Daniel Holmberg, Manu Airaksinen, Viviana Marchi +5
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems that may need prompt interventions. Spontaneous motor activity, or '…