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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
Stock Price Prediction using Dynamic Neural Networks—0
Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference—0
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks—0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection—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
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