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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
Nonlinear Systems Identification Using Deep Dynamic Neural NetworksCode0
Fixing Overconfidence in Dynamic Neural NetworksCode0
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networksCode0
Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural NetworksCode0
Monadic Deep Learning—0
Neuroevolving Electronic Dynamical Networks—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
Parametric Taylor series based latent dynamics identification neural networks—0
Siamese Labels Auxiliary Learning—0
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