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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
Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi III : Return of the Ninja Master—0
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks—0
Analysis of Memory Organization for Dynamic Neural Networks—0
An Introduction to Cognidynamics—0
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
A Survey on Dynamic Neural Networks for Natural Language Processing—0
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks—0
DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization—0
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference—0
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks—0
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