1 citations · 1 across the 5 of their papers we have counts for
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Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias
Mikhail Krasnov, Blaž BertalaniÄ, 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…
A Network Science Approach to Granular Time Series Segmentation
Ivana KesiÄ, Carolina Fortuna, Mihael MohorÄiÄ +1
Time series segmentation (TSS) is one of the time series (TS) analysis techniques, that has received considerably less attention compared to other TS related tasks. In recent years…
Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification
Irina BaraÅ¡in, Blaž BertalaniÄ, Mihael MohorÄiÄ +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…
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…
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 Äop, Blaž BertalaniÄ, 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…