Jiawei Liu

According to our database, Jiawei Liu authored at least 25 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits.

Shan Shen, Shenglu Hua, Jiajun Zou, Jiawei Liu, Jianwang Zhai, Chuan Shi, Wenjian Yu

CoRR, July, 2025

Graph Foundation Models: Concepts, Opportunities and Challenges.

Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi

IEEE Trans. Pattern Anal. Mach. Intell., June, 2025

ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA.

Zhengyuan Shi, Zeju Li, Chengyu Ma, Yunhao Zhou, Ziyang Zheng, Jiawei Liu, Hongyang Pan, Lingfeng Zhou, Kezhi Li, Jiaying Zhu, Lingwei Yan, Zhiqiang He, Chenhao Xue, Wentao Jiang, Fan Yang, Guangyu Sun, Xiaoyan Yang, Gang Chen, Chuan Shi, Zhufei Chu, Jun Yang, Qiang Xu

CoRR, May, 2025

Graph neural network based time estimator for SAT solver.

Jiawei Liu, Wenyi Xiao, Hongtao Cheng, Chuan Shi

Int. J. Mach. Learn. Cybern., February, 2025

Graph Foundation Models for Recommendation: A Comprehensive Survey.

Bin Wu, Yihang Wang, Yuanhao Zeng, Jiawei Liu, Jiashu Zhao, Cheng Yang, Yawen Li, Long Xia, Dawei Yin, Chuan Shi

CoRR, February, 2025

SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild.

Jiawei Liu, Yuanzhi Zhu, Feiyu Gao, Zhibo Yang, Peng Wang, Junyang Lin, Xinggang Wang, Wenyu Liu

CoRR, January, 2025

MILS: Modality Interaction Driven Learning for Logic Synthesis.

Mingyu Zhao, Jiawei Liu, Jianwang Zhai, Chuan Shi

Proceedings of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025, New Orleans, LA, USA, 30 June 2025, 2025

WideGate: Beyond Directed Acyclic Graph Learning in Subcircuit Boundary Prediction.

Jiawei Liu, Zhiyan Liu, Xun He, Jianwang Zhai, Zhengyuan Shi, Qiang Xu, Bei Yu, Chuan Shi

Proceedings of the Design, Automation & Test in Europe Conference, 2025

IR-Fusion: A Fusion Framework for Static IR Drop Analysis Combining Numerical Solution and Machine Learning.

Feng Guo, Jianwang Zhai, Jingyu Jia, Jiawei Liu, Kang Zhao, Bei Yu, Chuan Shi

Proceedings of the Design, Automation & Test in Europe Conference, 2025

2024

Graph foundation model.

Chuan Shi, Junze Chen, Jiawei Liu, Cheng Yang

Frontiers Comput. Sci., December, 2024

Stabilized activation scale estimation for precise Post-Training Quantization.

Zhenyang Hao, Xinggang Wang, Jiawei Liu, Zhihang Yuan, Dawei Yang, Wenyu Liu

Neurocomputing, February, 2024

Endowing Pre-trained Graph Models with Provable Fairness.

Zhongjian Zhang, Mengmei Zhang, Yue Yu, Cheng Yang, Jiawei Liu, Chuan Shi

Proceedings of the ACM on Web Conference 2024, 2024

PolarGate: Breaking the Functionality Representation Bottleneck of And-Inverter Graph Neural Network.

Jiawei Liu, Jianwang Zhai, Mingyu Zhao, Zhe Lin, Bei Yu, Chuan Shi

Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design, 2024

PGAU: Static IR Drop Analysis for Power Grid using Attention U-Net Architecture and Label Distribution Smoothing.

Feng Guo, Jiawei Liu, Jianwang Zhai, Jingyu Jia, Kang Zhao, Chuan Shi

Proceedings of the Great Lakes Symposium on VLSI 2024, 2024

Visual Text Generation in the Wild.

Yuanzhi Zhu, Jiawei Liu, Feiyu Gao, Wenyu Liu, Xinggang Wang, Peng Wang, Fei Huang, Cong Yao, Zhibo Yang

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Towards Graph Foundation Models: A Survey and Beyond.

Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi

CoRR, 2023

Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric.

Lin Niu, Jiawei Liu, Zhihang Yuan, Dawei Yang, Xinggang Wang, Wenyu Liu

CoRR, 2023

RPTQ: Reorder-based Post-training Quantization for Large Language Models.

Zhihang Yuan, Lin Niu, Jiawei Liu, Wenyu Liu, Xinggang Wang, Yuzhang Shang, Guangyu Sun, Qiang Wu, Jiaxiang Wu, Bingzhe Wu

CoRR, 2023

Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance.

Zhihang Yuan, Jiawei Liu, Jiaxiang Wu, Dawei Yang, Qiang Wu, Guangyu Sun, Wenyu Liu, Xinggang Wang, Bingzhe Wu

CoRR, 2023

Learning to Distill Graph Neural Networks.

Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Abnormal Event Detection via Hypergraph Contrastive Learning.

Bo Yan, Cheng Yang, Chuan Shi, Jiawei Liu, Xiaochen Wang

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

PD-Quant: Post-Training Quantization Based on Prediction Difference Metric.

Jiawei Liu, Lin Niu, Zhihang Yuan, Dawei Yang, Xinggang Wang, Wenyu Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources.

Jiawei Liu, Chuan Shi, Cheng Yang, Zhiyuan Lu, Philip S. Yu

AI Open, January, 2022

2021

Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework.

Cheng Yang, Jiawei Liu, Chuan Shi

Proceedings of the WWW '21: The Web Conference 2021, 2021

2020

Decorrelated Clustering with Data Selection Bias.

Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020