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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
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks—0
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networksCode0
Temporal Domain Generalization with Drift-Aware Dynamic Neural NetworksCode1
A Survey on Dynamic Neural Networks for Natural Language Processing—0
DYNASHARE: DYNAMIC NEURAL NETWORKS FOR MULTI-TASK LEARNINGCode0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—0
Siamese Labels Auxiliary Learning—0
Dynamic Neural Networks: A Survey—0
Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck ApproachCode1
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