• Explainability in subgraphs-enhanced Graph Neural Networks 

      Guerra, Michele; Bianchi, Filippo Maria; Scardapane, Simone; Spinelli, Indro (Journal article; Tidsskriftartikkel, 2023)
      Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of Graph Neural Networks (GNNs), which was proved to be not higher than the 1-dimensional Weisfeiler-Leman isomorphism test. The new paradigm suggests using subgraphs extracted from the input graph to improve the model’s expressiveness, but the additional complexity exacerbates an ...
    • Probabilistic load forecasting with Reservoir Computing 

      Guerra, Michele; Bianchi, Filippo Maria; Scardapane, Simone (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-15)
      Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt ...
    • Probabilistic Load Forecasting With Reservoir Computing 

      Guerra, Michele; Bianchi, Filippo Maria; Scardapane, Simone (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-15)
      Some applications of deep learning require not only to provide accurate results but also to quantify the amount of confidence in their prediction. The management of an electric power grid is one of these cases: to avoid risky scenarios, decision-makers need both precise and reliable forecasts of, for example, power loads. For this reason, point forecasts are not enough hence it is necessary to adopt ...
    • Reservoir computing approaches for representation and classification of multivariate time series 

      Bianchi, Filippo Maria; Scardapane, Simone; Løkse, Sigurd; Jenssen, Robert (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-29)
      Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers. Despite their unrivaled training speed, MTS classifiers based on a standard RC ...