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ORCIDDai Liu
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2020 – today
- 2025
[c6]Xiaorang Guo, Tigran Bunarjyan, Dai Liu, Benjamin Lienhard, Martin Schulz:
KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA. DAC 2025: 1-7- [i6]Xiaorang Guo, Tigran Bunarjyan, Dai Liu, Benjamin Lienhard, Martin Schulz:
KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA. CoRR abs/2503.03544 (2025) - [i5]Zekai Li, Xinhao Zhong, Samir Khaki, Zhiyuan Liang, Yuhao Zhou, Mingjia Shi, Ziqiao Wang, Xuanlei Zhao, Wangbo Zhao, Ziheng Qin, Mengxuan Wu, Pengfei Zhou, Haonan Wang, David Junhao Zhang, Jia-Wei Liu, Shaobo Wang, Dai Liu, Linfeng Zhang, Guang Li, Kun Wang, Zheng Zhu, Zhiheng Ma, Joey Tianyi Zhou, Jiancheng Lv, Yaochu Jin, Peihao Wang, Kaipeng Zhang, Lingjuan Lyu, Yiran Huang, Zeynep Akata, Zhiwei Deng, Xindi Wu, George Cazenavette, Yuzhang Shang
, Justin Cui, Jindong Gu, Qian Zheng, Hao Ye, Shuo Wang, Xiaobo Wang, Yan Yan, Angela Yao, Mike Zheng Shou, Tianlong Chen, Hakan Bilen, Baharan Mirzasoleiman, Manolis Kellis, Konstantinos N. Plataniotis, Zhangyang Wang, Bo Zhao, Yang You, Kai Wang:
DD-Ranking: Rethinking the Evaluation of Dataset Distillation. CoRR abs/2505.13300 (2025) - 2024
- [c5]Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, Martin Schulz:
Dataset Distillation by Automatic Training Trajectories. ECCV (87) 2024: 334-351 - [c4]Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz:
Reinforcement Learning-Driven Co-Scheduling and Diverse Resource Assignments on NUMA Systems. ICCD 2024: 170-178 - [i4]Issa Saba, Eishi Arima, Dai Liu, Martin Schulz:
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning. CoRR abs/2405.03831 (2024) - [i3]Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz:
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach. CoRR abs/2405.08754 (2024) - [i2]Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, Martin Schulz:
Dataset Distillation by Automatic Training Trajectories. CoRR abs/2407.14245 (2024) - 2023
- [j5]Ning Li, Dai Liu, Francis Boadu:
The impact of digital supply chain capabilities on enterprise sustainable competitive performance: an ambidextrous view. Ind. Manag. Data Syst. 123(6): 1670-1689 (2023) - [c3]Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz:
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach. CLUSTER 2023: 185-196 - [c2]Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, Alois Knoll:
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments. IJCNN 2023: 1-10 - 2022
- [c1]Issa Saba, Eishi Arima, Dai Liu, Martin Schulz:
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning. ARCS 2022: 51-67 - [i1]Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, Alois C. Knoll:
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments. CoRR abs/2208.11311 (2022) - 2020
- [j4]Dai Liu, Yong-Bo Zhao, Ziqian Yuan, Jie-tao Li, Guo-ji Chen:
Target tracking methods based on a signal-to-noise ratio model. Frontiers Inf. Technol. Electron. Eng. 21(12): 1804-1814 (2020)
2010 – 2019
- 2019
- [j3]Dai Liu, Yongbo Zhao, Baoqing Xu:
Tracking Algorithms Aided by the Pose of Target. IEEE Access 7: 9627-9633 (2019) - 2018
- [j2]Jing Quan, Bo Zeng, Dai Liu:
Green Supplier Selection for Process Industries Using Weighted Grey Incidence Decision Model. Complex. 2018: 4631670:1-4631670:12 (2018) - 2016
- [j1]Chloe LeGendre, Xueming Yu, Dai Liu, Jay Busch, Andrew Jones, Sumanta N. Pattanaik, Paul E. Debevec:
Practical multispectral lighting reproduction. ACM Trans. Graph. 35(4): 32:1-32:11 (2016)
Coauthor Index
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