Capturing Uncertainty over Time for Spiking Neural Networks by Exploiting Conformal Prediction Sets

Titel:: Capturing Uncertainty over Time for Spiking Neural Networks by Exploiting Conformal Prediction Sets
Dokumenttyp:: Konferenzbeitrag
Autor(en):: Scholz, Daniel; Emonds, Oliver; Kreutz, Felix; Gerhards, Pascal; Huang, Jiaxin; Knobloch, Klaus; Knoll, Alois; Mayr, Christian
Kongress- / Buchtitel:: 2024 International Conference on Machine Learning and Applications (ICMLA)
Ausrichter der Konferenz:: IEEE
Jahr:: 2024
Seiten:: 107--114
Volltext / DOI:: doi:10.1109/icmla61862.2024.00021
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