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Medientyp:
Elektronischer Konferenzbericht
Titel:
Layer-Wise Relevance Propagation for Echo State Networks Applied to Earth System Variability
Beteiligte:
Landt-Hayen, Marco
[VerfasserIn];
Kröger, Peer
[VerfasserIn];
Claus, Martin
[VerfasserIn];
Rath, Willi
[VerfasserIn]
Erschienen:
AIRCC Publishing Corporation, 2022
Umfang:
text
Sprache:
Englisch
DOI:
https://doi.org/10.5121/csit.2022.122008
Entstehung:
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification, speech recognition or time series prediction). However, these models tend to produce black-box results and are often difficult to interpret. Layer-wise relevance propagation (LRP) is a widely used technique to understand how ANN models come to their conclusion and to understand what a model has learned. Here, we focus on Echo State Networks (ESNs) as a certain type of recurrent neural networks, also known as reservoir computing. ESNs are easy to train and only require a small number of trainable parameters, but are still black-box models. We show how LRP can be applied to ESNs in order to open the black-box. We also show how ESNs can be used not only for time series prediction but also for image classification: Our ESN model serves as a detector for El Nino Southern Oscillation (ENSO) from sea surface temperature anomalies. ENSO is actually a well-known problem and has been extensively discussed before. But here we use this simple problem to demonstrate how LRP can significantly enhance the explainablility of ESNs.