Search Results for author:
Found 5 papers, 4 papers with code
Machine Unlearning by Suppressing Sample Contribution
Machine Unlearning (MU) is to forget data from a well-trained model, which is practically important due to the "right to be forgotten".
Online Continual Learning via Logit Adjusted Softmax
Online continual learning is a challenging problem where models must learn from a non-stationary data stream while avoiding catastrophic forgetting.
Trainable Weight Averaging: A General Approach for Subspace Training
Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance.
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores.
Query Attack by Multi-Identity Surrogates
Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates.
