Search Results for author:
Found 10 papers, 5 papers with code
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science.
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
Deep Graph Representation Learning and Optimization for Influence Maximization
Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users.
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
With poisoning equal to or less than 0. 5% of the target-class data and 0. 05% of the training set, we can train a model to classify test examples from arbitrary classes into the target class when the examples are patched with a backdoor trigger.
Ranked #1 on
Clean-label Backdoor Attack (0.05%)
on Tiny ImageNet
Watermarking Pre-trained Encoders in Contrastive Learning
It is challenging to migrate existing watermarking techniques from the classification tasks to the contrastive learning scenario, as the owner of the encoder lacks the knowledge of the downstream tasks which will be developed from the encoder in the future.
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
In this paper, we investigate the effectiveness of data augmentation techniques in mitigating backdoor attacks and enhancing DL models' robustness.
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
With more and more advanced adversarial attack methods have been developed, a quantity of corresponding defense solutions were designed to enhance the robustness of DNN models.
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wrong classification results.
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.
