Search Results for author:
Found 8 papers, 6 papers with code
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge.
Towards Stable Backdoor Purification through Feature Shift Tuning
Our analysis shows that with the low poisoning rate, the entanglement between backdoor and clean features undermines the effect of tuning-based defenses.
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments.
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Furthermore, RAP can be naturally combined with many existing black-box attack techniques, to further boost the transferability.
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis
Therefore, adversarial training for multiple perturbations (ATMP) is proposed to generalize the adversarial robustness over different perturbation types (in $\ell_1$, $\ell_2$, and $\ell_\infty$ norm-bounded perturbations).
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0
Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD.
Random Noise Defense Against Query-Based Black-Box Attacks
We conduct the theoretical analysis about the effectiveness of RND against query-based black-box attacks and the corresponding adaptive attacks.
