1 citations · 1 across the 12 of their papers we have counts for
13 papers
Robust Graph Clustering Network for Multiple Missing Data
Keyuan Qiu, Renda Han, Zhen Tang +6
Clustering on graphs where both node attributes and structural links are partially missing remains a challenging task. Existing methods typically rely on imputation-then-clustering…
LATTICE: Constraint-Directed Scheduling, Memory Planning, and Pipeline Refinement for NPUs
Runhao Liu, Minnan Pei, Fei Ding +5
General-purpose NPUs execute fine-grained command DAGs across heterogeneous compute and memory-transfer engines backed by finite, explicitly managed on-chip memories. This executio…
Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning
Zhenyu Yu, Yangchen Zeng, Chunlei Meng +2
Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-level metrics. We challenge thes…
FlashSign: Pose-Free Guidance for Efficient Sign Language Video Generation
Liuzhou Zhang, Zeyu Zhang, Biao Wu +10
Sign language plays a crucial role in bridging communication gaps between the deaf and hard-of-hearing communities. However, existing sign language video generation models often re…
TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
Rong Fu, Yibo Meng, Zeyu Zhang +4
Real-time schedulers must reason about tight deadlines under strict compute budgets. We present TempoNet, a reinforcement learning scheduler that pairs a permutation-invariant Tran…
GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search
Rong Fu, Jia Yee Tan, Chunlei Meng +4
As the burgeoning power requirements of sophisticated neural architectures escalate, the information retrieval community has recognized ecological sustainability as a pivotal prior…
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection
Wenxin Zhang, Ding Xu, Guangzhen Yao +6
Time series anomaly detection is critical for system monitoring and risk identification, across various domains, such as finance and healthcare. However, for most reconstruction-ba…
Addressing Noise and Stochasticity in Fraud Detection for Service Networks
Wenxin Zhang, Ding Xu, Xi Xuan +5
Fraud detection is crucial in social service networks to maintain user trust and improve service network security. Existing spectral graph-based methods address this challenge by l…
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
Wenxin Zhang, Xiaojian Lin, Wenjun Yu +7
Time series anomaly detection holds notable importance for risk identification and fault detection across diverse application domains. Unsupervised learning methods have become pop…
Dual-channel Heterophilic Message Passing for Graph Fraud Detection
Wenxin Zhang, Jingxing Zhong, Guangzhen Yao +4
Fraudulent activities have significantly increased across various domains, such as e-commerce, online review platforms, and social networks, making fraud detection a critical task.…
Dual Boost-Driven Graph-Level Clustering Network
John Smith, Wenxuan Tu, Junlong Wu +10
Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notabl…
MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction
Xuyin Qi, Zeyu Zhang, Huazhan Zheng +19
Bone density prediction via CT scans to estimate T-scores is crucial, providing a more precise assessment of bone health compared to traditional methods like X-ray bone density tes…
Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering
Renda Han, Guangzhen Yao, Wenxin Zhang +7
Graph-level clustering is a fundamental task of data mining, aiming at dividing unlabeled graphs into distinct groups. However, existing deep methods that are limited by pooling ha…