132 citations · 683 across the 107 of their papers we have counts for
17 papers · 1 filter
HSCP: A Two-Stage Spectral Clustering Framework for Resource-Constrained UAV Identification
Maoyu Wang, Yao Lu, Bo Zhou +4
With the rapid development of Unmanned Aerial Vehicles (UAVs) and the increasing complexity of low-altitude security threats, traditional UAV identification methods struggle to ext…
Provable Repair of Deep Neural Network Defects by Preimage Synthesis and Property Refinement
Jianan Ma, Jingyi Wang, Qi Xuan +1
It is known that deep neural networks may exhibit dangerous behaviors under various security threats (e.g., backdoor attacks, adversarial attacks and safety property violation) and…
The Structural Scalpel: Automated Contiguous Layer Pruning for Large Language Models
Yao Lu, Yuqi Li, Wenbin Xie +4
Although large language models (LLMs) have achieved revolutionary breakthroughs in many fields, their large model size and high computational cost pose significant challenges for p…
Few-shot Molecular Property Prediction: A Survey
Zeyu Wang, Tianyi Jiang, Huanchang Ma +6
AI-assisted molecular property prediction has become a promising technique in early-stage drug discovery and materials design in recent years. However, due to high-cost and complex…
DSPC: Dual-Stage Progressive Compression Framework for Efficient Long-Context Reasoning
Yaxin Gao, Yao Lu, Zongfei Zhang +3
Large language models (LLMs) have achieved remarkable success in many natural language processing (NLP) tasks. To achieve more accurate output, the prompts used to drive LLMs have…
LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods
Shaoheng Wang, Yao Lu, Yuqi Li +5
As a parameter efficient fine-tuning (PEFT) method, low-rank adaptation (LoRA) can save significant costs in storage and computing, but its strong adaptability to a single task is…
SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing
Qiuhao Liu, Ling Li, Yao Lu +3
Deep neural networks tend to memorize noisy labels, severely degrading their generalization performance. Although Mixup has demonstrated effectiveness in improving generalization a…
\emph{FoQuS}: A Forgetting-Quality Coreset Selection Framework for Automatic Modulation Recognition
Yao Lu, Chunfeng Sun, Dongwei Xu +3
Deep learning-based Automatic Modulation Recognition (AMR) model has made significant progress with the support of large-scale labeled data. However, when developing new models or…
DUSE: A Data Expansion Framework for Low-resource Automatic Modulation Recognition based on Active Learning
Yao Lu, Hongyu Gao, Zhuangzhi Chen +4
Although deep neural networks have made remarkable achievements in the field of automatic modulation recognition (AMR), these models often require a large amount of labeled data fo…
From LLM-anation to LLM-orchestrator: Coordinating Small Models for Data Labeling
Yao Lu, Zhaiyuan Ji, Jiawei Du +3
Although the annotation paradigm based on Large Language Models (LLMs) has made significant breakthroughs in recent years, its actual deployment still has two core bottlenecks: fir…
ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices
Maoyu Wang, Yao Lu, Jiaqi Nie +4
With the rapid development of deep learning, a growing number of pre-trained models have been publicly available. However, deploying these fixed models in real-world IoT applicatio…
FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition
Yao Lu, Tengfei Ma, Zeyu Wang +5
With the rapid development of wireless communications and the growing complexity of digital modulation schemes, traditional manual modulation recognition methods struggle to extrac…
Adaptive Substructure-Aware Expert Model for Molecular Property Prediction
Tianyi Jiang, Zeyu Wang, Shanqing Yu +1
Molecular property prediction is essential for applications such as drug discovery and toxicity assessment. While Graph Neural Networks (GNNs) have shown promising results by model…
Hierarchical Local-Global Feature Learning for Few-shot Malicious Traffic Detection
Songtao Peng, Lei Wang, Wu Shuai +4
With the rapid growth of internet traffic, malicious network attacks have become increasingly frequent and sophisticated, posing significant threats to global cybersecurity. Tradit…
Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection
Junhao Wu, Yixin Yang, Chengxiang Jin +5
With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the sec…
Mixture of Message Passing Experts with Routing Entropy Regularization for Node Classification
Xuanze Chen, Jiajun Zhou, Yadong Li +3
Graph neural networks (GNNs) have achieved significant progress in graph-based learning tasks, yet their performance often deteriorates when facing heterophilous structures where c…
Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph
Jiajun Zhou, Wentao Fu, Hao Song +2
As the Internet rapidly expands, the increasing complexity and diversity of network activities pose significant challenges to effective network governance and security regulation.…