Qi Xuan
Identifiers
- name variant Qi Xuan 0.60 · backfill
Papers (14)
- Provable Fairness Repair for Deep Neural Networks cs.SE · 2026 · author #3
- ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction cs.CE · 2026 · author #5
- Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals eess.SP · 2026 · author #5
- Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification cs.LG · 2026 · author #7
- Open DNN Box by Power Side-Channel Attack cs.CR · 2019 · author #9
- MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks cs.CV · 2019 · author #1
- N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network cs.IR · 2019 · author #7
- Can Adversarial Network Attack be Defended? cs.SI · 2019 · author #4
- E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction cs.SI · 2019 · author #7
- A Self-Learning Information Diffusion Model for Smart Social Networks cs.SI · 2018 · author #1
- GA Based Q-Attack on Community Detection cs.SI · 2018 · author #6
- Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations cs.SI · 2018 · author #6
- Fast Gradient Attack on Network Embedding physics.soc-ph · 2018 · author #6
- Converging Work-Talk Patterns in Online Task-Oriented Communities cs.SE · 2014 · author #1
Mentions
- 2605.19549 #3 · arxiv_oai · confidence 0.70 Qi Xuan
Frequent Coauthors
- Jinyin Chen 6 shared papers
- Shanqing Yu 5 shared papers
- Xiaoniu Yang 3 shared papers
- Yangyang Wu 3 shared papers
- Chenbo Fu 2 shared papers
- Guanrong Chen 2 shared papers
- Haibin Zheng 2 shared papers
- Minghao Zhao 2 shared papers
- Xiang Lin 2 shared papers
- Xincheng Shu 2 shared papers
- Xuanheng Xu 2 shared papers
- Yi Liu 2 shared papers
- Yixian Chen 2 shared papers
- Yun Xiang 2 shared papers
- Chengbo Fu 1 shared papers
- Chen Ma 1 shared papers
- Chuang Zhao 1 shared papers
- Dan Zhang 1 shared papers
- Fuxian Li 1 shared papers
- Haiyang Hao 1 shared papers