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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
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
Analysis of Memory Organization for Dynamic Neural Networks—0
Parametric Taylor series based latent dynamics identification neural networks—0
Cavs: A Vertex-centric Programming Interface for Dynamic 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
Fixing Overconfidence in Dynamic Neural NetworksCode0
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