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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 11–20 of 37 papers

TitleStatusHype
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines—0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—0
Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi III : Return of the Ninja Master—0
A Survey on Dynamic Neural Networks for Natural Language Processing—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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