Dai Liu

17 papers A* 1B 1C 2Journal 11Unranked 2
YearRankTypeTitle / Venue / Authors
2025 J jnl
DD-Ranking: Rethinking the Evaluation of Dataset Distillation.
CoRR
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
2025 A* conf
KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA.
DAC
Xiaorang Guo, Tigran Bunarjyan, Dai Liu, Benjamin Lienhard, Martin Schulz
2025 J jnl
KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA.
CoRR
Xiaorang Guo, Tigran Bunarjyan, Dai Liu, Benjamin Lienhard, Martin Schulz
2024 — conf
Dataset Distillation by Automatic Training Trajectories.
ECCV (87)
Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, Martin Schulz
2024 J jnl
Dataset Distillation by Automatic Training Trajectories.
CoRR
Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, Martin Schulz
2024 J jnl
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach.
CoRR
Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz
2024 J jnl
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning.
CoRR
Issa Saba, Eishi Arima, Dai Liu, Martin Schulz
2024 C conf
Reinforcement Learning-Driven Co-Scheduling and Diverse Resource Assignments on NUMA Systems.
ICCD
Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz
2023 B conf
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments.
IJCNN
Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, Alois Knoll
2023 C conf
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach.
CLUSTER
Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz
2023 J jnl
The impact of digital supply chain capabilities on enterprise sustainable competitive performance: an ambidextrous view.
Ind. Manag. Data Syst.
Ning Li, Dai Liu, Francis Boadu
2022 J jnl
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments.
CoRR
Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, Alois C. Knoll
2022 — conf
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning.
ARCS
Issa Saba, Eishi Arima, Dai Liu, Martin Schulz
2020 J jnl
Target tracking methods based on a signal-to-noise ratio model.
Frontiers Inf. Technol. Electron. Eng.
Dai Liu, Yong-Bo Zhao, Ziqian Yuan, Jie-tao Li, Guo-ji Chen
2019 J jnl
Tracking Algorithms Aided by the Pose of Target.
IEEE Access
Dai Liu, Yongbo Zhao, Baoqing Xu
2018 J jnl
Green Supplier Selection for Process Industries Using Weighted Grey Incidence Decision Model.
Complex.
Jing Quan, Bo Zeng, Dai Liu
2016 J jnl
Practical multispectral lighting reproduction.
ACM Trans. Graph.
Chloe LeGendre, Xueming Yu, Dai Liu, Jay Busch, Andrew Jones, Sumanta N. Pattanaik, Paul E. Debevec