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Found 8 papers, 2 papers with code
Teach LLMs to Phish: Stealing Private Information from Language Models
When large language models are trained on private data, it can be a significant privacy risk for them to memorize and regurgitate sensitive information.
Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue.
AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection
This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor.
Risk assessment and mitigation of e-scooter crashes with naturalistic driving data
The goal is to quantitatively measure the behaviors of e-scooter riders in different encounters to help facilitate crash scenario modeling, baseline behavior modeling, and the potential future development of in-vehicle mitigation algorithms.
Neurotoxin: Durable Backdoors in Federated Learning
In this type of attack, the goal of the attacker is to use poisoned updates to implant so-called backdoors into the learned model such that, at test time, the model's outputs can be fixed to a given target for certain inputs.
Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning
We model the HUDNs as a heterogeneous graph and train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning, in which the loss function is composed of the unsupervised part that helps the GNN approach the optimal representation function and the supervised part that utilizes the previous experience to reduce useless exploration.
ECS-Net: Improving Weakly Supervised Semantic Segmentation by Using Connections Between Class Activation Maps
Image-level weakly supervised semantic segmentation is a challenging task.
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
Replacing BN with the recently-proposed Group Normalization (GN) can reduce gradient diversity and improve test accuracy.
