May 14, 2024, 4:42 a.m. | Guillaume Pourcel, Mirko Goldmann, Ingo Fischer, Miguel C. Soriano

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07236v1 Announce Type: new
Abstract: Recurrent Neural Networks excel at predicting and generating complex high-dimensional temporal patterns. Due to their inherent nonlinear dynamics and memory, they can learn unbounded temporal dependencies from data. In a Machine Learning setting, the network's parameters are adapted during a training phase to match the requirements of a given task/problem increasing its computational capabilities. After the training, the network parameters are kept fixed to exploit the learned computations. The static parameters thereby render the network …

abstract arxiv control cs.lg data dependencies dynamics excel learn machine machine learning match memory network networks neural networks nlin.ao parameters patterns recurrent neural networks requirements temporal training type

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