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Pruning some connections from the reservoir to the readout layer can increase the generalization ability of the network and is thus a way of regularizing the output. Another way of regularizing the output is to shrink the weights of the connections from the reservoir to the readout layer.
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Abstract. Reservoir Computing is a new paradigm for using Recurrent. Neural Networks which shows promising results. However, as the recurrent.
Missing: regularization | Show results with:regularization
Reservoir computing is a new paradigm for using recurrent neural network with a much simpler training method. The key idea is to use a large but fixed recurrent ...
Abstract. Reservoir computing is a new paradigm for using recurrent neural network with a much simpler training method. The key idea is to use a large but fixed ...
Abstract. Reservoir Computing is a new paradigm to use artificial neural networks. Despite its promising performances, it has still some drawbacks:.
data regularization and reservoir size influences the resulting performance. Some techniques, like seasonal decomposition and a collective vote approach ...
Jan 16, 2024 · We propose a scheme that can enhance the performance and reduce the computational cost of quantum reservoir computing.
Mar 6, 2024 · Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations ...
Oct 17, 2023 · Pruning is a widely used technique to compress trained neural networks into much smaller models by removing unnecessary weights [13,14,15,16,17] ...
The two proposed methods, Recurrent Kernel and Structured Reservoir. Computing, turn out to be much faster and more memory-efficient than conventional.