Xiang Chen

Xiang Chen

Intelligence Fusion Laboratory

Archived Papers

  • [arxiv ‘26] † Z. Zheng, Z. Mao, S. Tian, M. Li, J. Chen, X. Sun, Z. Zhang, X. Liu, D. Cao, H. Mei, X. Chen. “HeiSD: Hybrid Speculative Decoding for Embodied Vision-Language-Action Models with Kinematic Awareness” arXiv:2603.17573, March 2026.
  • [arxiv ‘26] † Z. Zheng, S. Tian, H. Cao, C. Li, J. Chen, M. Li, X. Sun, H. Zou, G. Luo, X. Chen. “RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA models” arXiv:2603.07949, March 2026.
  • [arxiv ‘26] † Z. Zheng, H. Cao, S. Tian, J. Chen, M. Li, X. Sun, H. Zou, Z. Zhang, X. Liu, D. Cao, H. Mei, X. Chen. “DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models” arXiv:2603.07904, March 2026.
  • [arxiv ‘26] † Z. Zheng, Z. Mao, X. Zhou, J. Chen, M. Li, X. Sun, H. Zou, Z. Zhang, X. Liu, D. Cao, H. Mei, X. Chen. “VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness” arXiv:2603.07080, March 2026.
  • [arxiv ‘26] † X. Sun, M. Li, Z. Zheng, J. Chen, H. Xu, Y. Liang, X. Chen. “STaRR:Spatial-Temporal Token-Dynamics-Aware Responsive Remasking for Diffusion Language Models” arXiv:2601.04205, January 2026.
  • [arxiv ‘25] † Z. Zheng, Z. Wang, X. Cui, M. Li, J. Chen, A. Li, X. Chen. “FedHQ: Hybrid Runtime Quantization for Federated Learning” arXiv:2505.11982, May 2025.
  • [arxiv ‘24] † Z. Zheng, Y. Li, J. Chen, P. Zhou, X. Chen, Y. Liu. “Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference” arXiv:2412.13902, December 2024.
  • [arXiv ‘23] † M. Zhang, F. Yu, Y. Yu, M. Zhang, A. Li, and X. Chen. “FedHC: A Scalable Federated Learning Framework for Heterogeneous and Resource-Constrained Clients,” arXiv:2305.15668, May 2023.
  • [arXiv ‘23] † Y. Yu, F. Yu, M. Zhang, D. Wang, T. Soyata, C. Liu, and X. Chen. “GACER: Granularity-Aware ConcurrEncy Regulation for Multi-Tenant Deep Learning,” arXiv:2304.11745, Apr. 2023.
  • [arXiv ‘22] † F Yu, D Wang, L Shangguan, M Zhang, C Liu, and X Chen. “A Survey of Multi-Tenant Deep Learning Inference on GPU,” arXiv:2203.09040, Mar. 2022.
  • [arXiv ‘21] † F. Yu, D. Wang, L. Shangguan, M. Zhang, X. Tang, C. Liu, and X. Chen. “A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities,” arXiv:2111.14247, Nov. 2021.
  • [arXiv ‘20] † F. Yu, Z. Xu, T. Shen, D. Stamoulis, L. Shangguan, D. Wang, R. Madhok, C. Zhao, X. Li, N. Karianakis, D. Lymberopoulos, A. Li, C. Liu, Y. Chen, and X. Chen. “Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination,” arXiv:2011.03897, Nov. 2020.
  • [arXiv ‘20] † F. Yu, W. Zhang, Z. Qin, Z. Xu, D. Wang, C. Liu, Z. Tian, and X. Chen. “Heterogeneous Federated Learning,” arXiv:2008.06767, Aug. 2020.
  • [arXiv ‘18] S. Ye, T. Zhang, K. Zhang, J. Li, K. Xu, Y. Yang, F. Yu, J. Tang, M. Fardad, S. Liu, X. Chen, X. Lin, and Y. Wang. “Progressive Weight Pruning of Deep Neural Networks using ADMM,” arXiv:1810.07378, Oct. 2018.
  • [arXiv ‘18] † F. Yu, C. Liu, Y. Wang, L. Zhao, and X. Chen. “Interpreting Adversarial Robustness: A View from Decision Surface in Input Space,” arXiv:1810.00144, Oct. 2018.
  • [arXiv ‘18] † Z. Xu, F. Yu, C. Liu, and X. Chen. “HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition,” arXiv:1809.01697, Sep. 2018.
  • [arXiv ‘18] † F. Yu, Z. Xu, Y. Wang, C. Liu, and X. Chen. “Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients,” arXiv:1805.09370, May 2018.