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Found 12 papers, 6 papers with code
Interpreting Deep Learning-Based Networking Systems
While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable for network operators.
Multi-level Knowledge Distillation via Knowledge Alignment and Correlation
While it is important to transfer the full knowledge from teacher to student, we introduce the Multi-level Knowledge Distillation (MLKD) by effectively considering both knowledge alignment and correlation.
SvTPM: A Secure and Efficient vTPM in the Cloud
In this paper, we present SvTPM, a secure and efficient software-based vTPM implementation based on hardware-rooted Trusted Execution Environment (TEE), providing a whole life cycle protection of vTPMs in the cloud.
Cryptography and Security
CARTL: Cooperative Adversarially-Robust Transfer Learning
In deep learning, a typical strategy for transfer learning is to freeze the early layers of a pre-trained model and fine-tune the rest of its layers on the target domain.
Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models
HATEGUARD further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate.
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks.
Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards
Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment.
Rallying Adversarial Techniques against Deep Learning for Network Security
Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning approaches for use in network intrusion detection systems.
Understanding and Measuring Robustness of Multimodal Learning
We then introduce a new type of multimodal adversarial attacks called decoupling attack in MUROAN that aims to compromise multimodal models by decoupling their fused modalities.
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model
In this paper, we propose Multi-level Multilingual Knowledge Distillation (MMKD), a novel method for improving multilingual language models.
An Investigation of Large Language Models for Real-World Hate Speech Detection
Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques.
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
Online user-generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.
