Search Results for author:
Found 3 papers, 1 papers with code
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning.
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
For those reasons, there is a high demand for trustworthy and rigorous methods to verify the robustness of neural network models.
Supervised Robustness-preserving Data-free Neural Network Pruning
This may be unrealistic in practice, as the data controllers are often reluctant to provide their model consumers with the original data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
