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EZClone: Improving DNN Model Extraction Attack via Shape Distillation from GPU Execution Profiles
Prior work has shown that, once a DNN has been successfully cloned, further attacks such as model evasion or model inversion can be accelerated significantly.
Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning
The prevalence and success of Deep Neural Network (DNN) applications in recent years have motivated research on DNN compression, such as pruning and quantization.
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation
Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups.
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