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Dynamic neural networks

Dynamic neural networks are adaptable models that can change their structure or parameters during training or inference based on input complexity or computational constraints. They offer benefits like improved efficiency, adaptability, and scalability compared to static architectures.

Papers

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Showing 21–30 of 37 papers

TitleStatusHype
Parametric Taylor series based latent dynamics identification neural networks—0
Siamese Labels Auxiliary Learning—0
Stock Price Prediction using Dynamic Neural Networks—0
Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference—0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection—0
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison—0
GradMDM: Adversarial Attack on Dynamic Networks—0
Long-Distance Gesture Recognition using Dynamic Neural Networks—0
Monadic Deep Learning—0
Neuroevolving Electronic Dynamical Networks—0
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