Zihan Wu
This page is a disambiguation page, it actually contains mutiple papers from persons of the same or a similar name.
Known people with the same name:
- Zihan Wu 001 (Xiamen University, China)
- Zihan Wu 002 (University of Michigan-Ann Arbor, School of Information, MI, USA)
- Zihan Wu 003 (Jiangsu University, Zhenjiang, China)
- Zihan Wu 004 (China Agricultural University, Beijing, China)
- Zihan Wu 005 (Fudan University, State Key Laboratory of ASIC & System, Shanghai, China)
- Zihan Wu 006 (Tsinghua University, School of Integrated Circuits, Beijing, China)
- Zihan Wu 007 (Shanghai Rongyue Private Entry-Exit Services Co., Ltd, China)
- Zihan Wu 008 (Hebei University, School of Cyber Security and Computer, Baoding, China)
- Zihan Wu 009 (EPFL, Lausanne, Switzerland)
- Zihan Wu 010 (Harbin Institute of Technology, School of Electronics and Information Engineering / School of Information Science and Engineering, China)
- Zihan Wu 011 (Shenyang University of Technology, College of Information Science and Engineering, China)
- Zihan Wu 012 (Zhejiang University, School of Mechanical Engineering, Hangzhou, China)
Bibliography
2024
X-Shard: Optimistic Cross-Shard Transaction Processing for Sharding-Based Blockchains.
IEEE Trans. Parallel Distributed Syst., April, 2024
A 4-bit Calibration-Free Computing-In-Memory Macro With 3T1C Current-Programed Dynamic-Cascode Multi-Level-Cell eDRAM.
IEEE J. Solid State Circuits, March, 2024
Distributed Ledger Technol. Res. Pract., March, 2024
Int. J. Pattern Recognit. Artif. Intell., January, 2024
Multim. Tools Appl., 2024
Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models.
CoRR, 2024
Comput. Biol. Medicine, 2024
30.5 A Variation-Tolerant In-eDRAM Continuous-Time Ising Machine Featuring 15-Level Coefficients and Leaked Negative-Feedback Annealing.
Proceedings of the IEEE International Solid-State Circuits Conference, 2024
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024
2023
Semi-supervised Semantic Segmentation Meets Masked Modeling: Fine-grained Locality Learning Matters in Consistency Regularization.
CoRR, 2023