34 citations · 77 across the 11 of their papers we have counts for
12 papers
On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
Yongjian Guo, Wanlun Ma, Lingyu Shen +2
Fine-tuning is the dominant paradigm for specializing large language models (LLMs), yet it exposes a critical vulnerability: malicious data providers can embed harmful behaviors in…
Incremental Context-free Grammar Inference in Black Box Settings
Feifei Li, Xiao Chen, Xi Xiao +4
Black-box context-free grammar inference presents a significant challenge in many practical settings due to limited access to example programs. The state-of-the-art methods, Arvada…
PMG : Personalized Multimodal Generation with Large Language Models
Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao +2
The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both th…
Genos: General In-Network Unsupervised Intrusion Detection by Rule Extraction
Ruoyu Li, Qing Li, Yu Zhang +3
Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput, lack…
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
Zhutian Lin, Junwei Pan, Shangyu Zhang +5
Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for tre…
CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection
Hao Wang, Zeyu Gao, Chao Zhang +4
Binary code similarity detection (BCSD) is a fundamental technique for various application. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but h…
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
Hao Wang, Zeyu Gao, Chao Zhang +7
Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and…
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
Haozhen Zhang, Xi Xiao, Le Yu +3
As network security receives widespread attention, encrypted traffic classification has become the current research focus. However, existing methods conduct traffic classification…
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
Liangcai Su, Fan Yan, Jieming Zhu +5
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to…
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
Kelong Mao, Jieming Zhu, Xi Xiao +3
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies i…
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Kelong Mao, Jieming Zhu, Jinpeng Wang +4
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction e…
Learnable Hypergraph Laplacian for Hypergraph Learning
Jiying Zhang, Yuzhao Chen, Xi Xiao +2
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data. However, most existing conv…