9 citations · 33 across the 19 of their papers we have counts for
11 papers · 1 filter
Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias
Mikhail Krasnov, Blaž Bertalanič, Carolina Fortuna
Monotonicity has been a long-running architectural inductive bias for neural networks, motivated by tabular, scientific, and economic settings where outputs are known to respond mo…
Data Model Design for Explainable Machine Learning-based Electricity Applications
Carolina Fortuna, Gregor Cerar, Blaz Bertalanic +2
The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital tran…
MRM3: Machine Readable ML Model Metadata
Andrej Čop, Blaž Bertalanič, Marko Grobelnik +1
As the complexity and number of machine learning (ML) models grows, well-documented ML models are essential for developers and companies to use or adapt them to their specific use…
A Network Science Approach to Granular Time Series Segmentation
Ivana Kesić, Carolina Fortuna, Mihael Mohorčič +1
Time series segmentation assigns a label to each part of a sequence. We formulate dense univariate segmentation as node classification on a graph whose nodes are the original time…
A Representation Learning Approach to Feature Drift Detection in Wireless Networks
Athanasios Tziouvaras, Blaz Bertalanic, George Floros +3
AI is foreseen to be a centerpiece in next generation wireless networks enabling enabling ubiquitous communication as well as new services. However, in real deployment, feature dis…
Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification
Irina Barašin, Blaž Bertalanič, Mihael Mohorčič +1
Time series classification is a relevant step supporting decision-making processes in various domains, and deep neural models have shown promising performance in this respect. Desp…