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Dynamic neural networks
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Parametric Taylor series based latent dynamics identification neural networks
An Introduction to Cognidynamics
DyFADet: Dynamic Feature Aggregation for Temporal Action Detection
Neuroevolving Electronic Dynamical Networks
Dynamic DNNs and Runtime Management for Efficient Inference on Mobile/Embedded Devices
Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference
Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN
Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks
Long-Distance Gesture Recognition using Dynamic Neural Networks
Monadic Deep Learning
DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization
Stock Price Prediction using Dynamic Neural Networks
Evolving Artificial Neural Networks To Imitate Human Behaviour In Shinobi III : Return of the Ninja Master
GradMDM: Adversarial Attack on Dynamic Networks
Fixing Overconfidence in Dynamic Neural Networks
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
Boosted Dynamic Neural Networks
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information
On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks
SATBench: Benchmarking the speed-accuracy tradeoff in object recognition by humans and dynamic neural networks
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks
A Survey on Dynamic Neural Networks for Natural Language Processing
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network
Siamese Labels Auxiliary Learning
Dynamic Neural Networks: A Survey
Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Nonlinear Systems Identification Using Deep Dynamic Neural Networks
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