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Wei W. Xing
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2020 – today
- 2024
- [c18]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. AISTATS 2024: 2413-2421 - [c17]Shibo Li, Xin Yu, Wei W. Xing, Robert M. Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. AISTATS 2024: 2440-2448 - [i14]Wei W. Xing, Weijian Fan, Zhuohua Liu, Yuan Yao, Yuanqi Hu:
KATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology. CoRR abs/2404.14433 (2024) - 2023
- [b1]Akeel A. Shah, Puiki Leung, Qian Xu, Pang-Chieh Sui, Wei W. Xing:
New Paradigms in Flow Battery Modelling. Springer 2023, ISBN 978-981-99-2523-0, pp. 1-326 - [j4]Wei W. Xing, Xiang Jin, Tian Feng, Dan Niu, Weisheng Zhao, Zhou Jin:
BoA-PTA: A Bayesian Optimization Accelerated PTA Solver for SPICE Simulation. ACM Trans. Design Autom. Electr. Syst. 28(2): 27:1-27:26 (2023) - [c16]Shuo Yin, Guohao Dai, Wei W. Xing:
High-Dimensional Yield Estimation Using Shrinkage Deep Features and Maximization of Integral Entropy Reduction. ASP-DAC 2023: 283-289 - [c15]Yanfang Liu, Guohao Dai, Wei W. Xing:
Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold. DAC 2023: 1-6 - [c14]Wei W. Xing, Zheng Xing, Rongqi Lu, Zhelong Wang, Ning Xu, Yuanqing Cheng, Weisheng Zhao:
TOTAL: Multi-Corners Timing Optimization Based on Transfer and Active Learning. DAC 2023: 1-6 - [c13]Yanfang Liu, Guohao Dai, Yuanqing Cheng, Wang Kang, Wei W. Xing:
OPT: Optimal Proposal Transfer for Efficient Yield Optimization for Analog and SRAM Circuits. ICCAD 2023: 1-9 - [i13]Yuxin Wang, Zheng Xing, Wei W. Xing:
GAR: Generalized Autoregression for Multi-Fidelity Fusion. CoRR abs/2301.05729 (2023) - [i12]Yuwen Deng, Wang Kang, Wei W. Xing:
Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning. CoRR abs/2306.06904 (2023) - [i11]Yanfang Liu, Guohao Dai, Wei W. Xing:
Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold. CoRR abs/2307.15773 (2023) - [i10]Shibo Li, Xin Yu, Wei W. Xing, Mike Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. CoRR abs/2309.16971 (2023) - [i9]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR abs/2310.05387 (2023) - 2022
- [j3]Yi Gu, Chao Han, Yuhan Chen, Wei W. Xing:
Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 15: 594-608 (2022) - [c12]Zheng Wang, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Physics Informed Deep Kernel Learning. AISTATS 2022: 1206-1218 - [c11]Zhou Jin, Haojie Pei, Yichao Dong, Xiang Jin, Xiao Wu, Wei W. Xing, Dan Niu:
Accelerating nonlinear DC circuit simulation with reinforcement learning. DAC 2022: 619-624 - [c10]Shuo Yin, Xiang Jin, Linxu Shi, Kang Wang, Wei W. Xing:
Efficient bayesian yield analysis and optimization with active learning. DAC 2022: 1195-1200 - [c9]Shihong Wang, Xueying Zhang, Yichen Meng, Wei W. Xing:
E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction. IJCNN 2022: 1-8 - [c8]Yuxin Wang, Zheng Xing, Wei W. Xing:
GAR: Generalized Autoregression for Multi-Fidelity Fusion. NeurIPS 2022 - [i8]Shuo Yin, Guohao Dai, Wei W. Xing:
High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction. CoRR abs/2212.02100 (2022) - 2021
- [j2]Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Deep coregionalization for the emulation of simulation-based spatial-temporal fields. J. Comput. Phys. 428: 109984 (2021) - [c7]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. AISTATS 2021: 847-855 - [i7]Wei W. Xing, Akeel A. Shah, Peng Wang, Shandian Zhe, Qian Fu, Robert M. Kirby:
Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations. CoRR abs/2104.03743 (2021) - [i6]Wei W. Xing, Xiang Jin, Yi Liu, Dan Niu, Weishen Zhao, Zhou Jin:
BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver. CoRR abs/2108.00257 (2021) - 2020
- [c6]Wei W. Xing, Shireen Y. Elhabian, Robert Michael Kirby, Ross T. Whitaker, Shandian Zhe:
Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations. AAAI 2020: 6462-6469 - [c5]Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Scalable Gaussian Process Regression Networks. IJCAI 2020: 2456-2462 - [c4]Shibo Li, Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. NeurIPS 2020 - [i5]Shibo Li, Wei W. Xing, Mike Kirby, Shandian Zhe:
Scalable Variational Gaussian Process Regression Networks. CoRR abs/2003.11489 (2020) - [i4]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. CoRR abs/2006.04972 (2020) - [i3]Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe:
Physics Regularized Gaussian Processes. CoRR abs/2006.04976 (2020) - [i2]Shibo Li, Wei W. Xing, Mike Kirby, Shandian Zhe:
Multi-Fidelity Bayesian Optimization via Deep Neural Networks. CoRR abs/2007.03117 (2020)
2010 – 2019
- 2019
- [c3]Shandian Zhe, Wei W. Xing, Robert M. Kirby:
Scalable High-Order Gaussian Process Regression. AISTATS 2019: 2611-2620 - [c2]Jian Wang, Wei W. Xing, Robert M. Kirby, Miaomiao Zhang:
Data-Driven Model Order Reduction for Diffeomorphic Image Registration. IPMI 2019: 694-705 - [i1]Wei W. Xing, Robert M. Kirby, Shandian Zhe:
Deep Coregionalization for the Emulation of Spatial-Temporal Fields. CoRR abs/1910.07577 (2019) - 2017
- [c1]Wei W. Xing, Akeel A. Shah, Barbara Urasinska-Wojcik, Julian W. Gardner:
Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling. IEEE SENSORS 2017: 1-3 - 2016
- [j1]Wei W. Xing, Vasileios Triantafyllidis, Akeel A. Shah, Prasanth B. Nair, Nicholas Zabaras:
Manifold learning for the emulation of spatial fields from computational models. J. Comput. Phys. 326: 666-690 (2016)
Coauthor Index
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last updated on 2024-05-27 23:32 CEST by the dblp team
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