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2020 – today
- 2023
[c12]Zihu Wang, Hanbin Hu, Chen He
, Peng Li:
Recognizing Wafer Map Patterns Using Semi-Supervised Contrastive Learning with Optimized Latent Representation Learning and Data Augmentation. ITC 2023: 141-150- [i4]Zihu Wang, Yu Wang, Hanbin Hu, Peng Li:
Contrastive Learning with Consistent Representations. CoRR abs/2302.01541 (2023) - 2022
- [c11]Austin J. He, Hanbin Hu:
Identifying Covid-19 Chest X-Rays by Image-Based Deep Learning. ICMLT 2022: 112-118 - 2021
- [b1]Hanbin Hu:
Machine Learning Techniques for Rare Failure Detection in Analog and Mixed-Signal Verification and Test. University of California, Santa Barbara, USA, 2021 - [c10]Myung Seok Shim, Hanbin Hu, Peng Li:
Reversible Gating Architecture for Rare Failure Detection of Analog and Mixed-Signal Circuits. DAC 2021: 901-906 - [c9]Karthik Somayaji N. S., Hanbin Hu, Peng Li:
Prioritized Reinforcement Learning for Analog Circuit Optimization With Design Knowledge. DAC 2021: 1231-1236 - [c8]Hanbin Hu, Chen He, Peng Li:
Semi-supervised Wafer Map Pattern Recognition using Domain-Specific Data Augmentation and Contrastive Learning. ITC 2021: 113-122 - [c7]Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin:
Exponential Graph is Provably Efficient for Decentralized Deep Training. NeurIPS 2021: 13975-13987 - [i3]Bicheng Ying, Kun Yuan, Yiming Chen, Hanbin Hu, Pan Pan, Wotao Yin:
Exponential Graph is Provably Efficient for Decentralized Deep Training. CoRR abs/2110.13363 (2021) - [i2]Bicheng Ying, Kun Yuan, Hanbin Hu, Yiming Chen, Wotao Yin:
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning. CoRR abs/2111.04287 (2021) - 2020
- [j2]Peng Jing, Hanbin Hu, Fengping Zhan, Yuexia Chen, Yuji Shi:
Agent-Based Simulation of Autonomous Vehicles: A Systematic Literature Review. IEEE Access 8: 79089-79103 (2020) - [c6]Hanbin Hu, Nguyen Nguyen, Chen He, Peng Li:
Advanced Outlier Detection Using Unsupervised Learning for Screening Potential Customer Returns. ITC 2020: 1-10
2010 – 2019
- 2019
- [c5]Hanbin Hu, Peng Li, Jianhua Z. Huang:
Enabling High-Dimensional Bayesian Optimization for Efficient Failure Detection of Analog and Mixed-Signal Circuits. DAC 2019: 17 - [i1]Hanbin Hu, Mit Shah, Jianhua Z. Huang, Peng Li:
Global Adversarial Attacks for Assessing Deep Learning Robustness. CoRR abs/1906.07920 (2019) - 2018
- [c4]Hanbin Hu, Qingran Zheng, Ya Wang, Peng Li:
HFMV: hybridizing formal methods and machine learning for verification of analog and mixed-signal circuits. DAC 2018: 95:1-95:6 - [c3]Hanbin Hu, Peng Li, Jianhua Z. Huang:
Parallelizable Bayesian optimization for analog and mixed-signal rare failure detection with high coverage. ICCAD 2018: 98 - 2017
- [j1]Guoyong Shi, Hanbin Hu, Shuwen Deng:
Topological Approach to Automatic Symbolic Macromodel Generation for Analog Integrated Circuits. ACM Trans. Design Autom. Electr. Syst. 22(3): 47:1-47:25 (2017) - 2015
- [c2]Hanbin Hu, Guoyong Shi, Andy Tai, Frank Lee:
Topological symbolic simplification for analog design. ISCAS 2015: 2644-2647 - 2013
- [c1]Hanbin Hu, Guoyong Shi, Yan Zhu:
Incremental symbolic construction for topological modeling of analog circuits. ASICON 2013: 1-4
Coauthor Index
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