EAN: 9783959086486

Bilder-Quelle: discount24.de - Sport-Freizeit

In an era of complex deep learning architectures like transformers CNNs and LSTM cells the challenge persists: the hunger for labeled data and high energy. This dissertation explores Echo State Network (ESN) an RNN variant. ESN's efficiency in linear regression training and simplicity suggest pathways to resource-efficient adaptable deep learning. Systematically deconstructing ESN architecture into flexible modules it introduces basic ESN models with random weights and efficient deterministic ESN models as baselines. Diverse unsupervised pre-training methods for ESN components are evaluated against these baselines. Rigorous benchmarking across datasets - time-series classification audio recognition - shows competitive performance of ESN models with state-of-the-art approaches. Identified nuanced use cases guiding model preferences and limitations in training methods highlight the importance of proposed ESN models in bridging reservoir computing and deep learning.

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20.03.2025 um 16:13 Uhr

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