28 citations · 51 across the 7 of their papers we have counts for
7 papers
Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights
Jiajia Xie, Sheng Zhang, Beihao Xia +5
Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajecto…
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Mengxuan Li, Ke Liu, Hongyang Chen +3
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this t…
Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques
Yulei Zhang, Cen Mo, Xiang Chen +4
Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to…
Scalable Algorithms for Laplacian Pseudo-inverse Computation
Meihao Liao, Rong-Hua Li, Qiangqiang Dai +2
The pseudo-inverse of a graph Laplacian matrix, denoted as , finds extensive application in various graph analysis tasks. Notable examples include the calculation of ele…
Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications
Ercong Yu, Jinle Zhu, Qiang Li +4
This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-inpu…
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling
Hao Wang, Zhi-Qi Cheng, Jingdong Sun +4
Multi-view or even multi-modal data is appealing yet challenging for real-world applications. Detecting anomalies in multi-view data is a prominent recent research topic. However,…
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data
Ning Ma, Jiajun Bu, Zhen Zhang +1
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data. However, the source data are not al…