Wei W. Xing

Orcid: 0000-0002-3177-8478

According to our database1, Wei W. Xing authored at least 29 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
BoA-PTA: A Bayesian Optimization Accelerated PTA Solver for SPICE Simulation.
ACM Trans. Design Autom. Electr. Syst., March, 2023

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels.
CoRR, 2023

Multi-Resolution Active Learning of Fourier Neural Operators.
CoRR, 2023

Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning.
CoRR, 2023

OPT: Optimal Proposal Transfer for Efficient Yield Optimization for Analog and SRAM Circuits.
Proceedings of the IEEE/ACM International Conference on Computer Aided Design, 2023

TOTAL: Multi-Corners Timing Optimization Based on Transfer and Active Learning.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

High-Dimensional Yield Estimation Using Shrinkage Deep Features and Maximization of Integral Entropy Reduction.
Proceedings of the 28th Asia and South Pacific Design Automation Conference, 2023

New Paradigms in Flow Battery Modelling
16, Springer, ISBN: 978-981-99-2523-0, 2023

2022
Mission Replanning for Multiple Agile Earth Observation Satellites Based on Cloud Coverage Forecasting.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

GAR: Generalized Autoregression for Multi-Fidelity Fusion.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

E-LMC: Extended Linear Model of Coregionalization for Spatial Field Prediction.
Proceedings of the International Joint Conference on Neural Networks, 2022

Efficient bayesian yield analysis and optimization with active learning.
Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

Accelerating nonlinear DC circuit simulation with reinforcement learning.
Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

Physics Informed Deep Kernel Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Deep coregionalization for the emulation of simulation-based spatial-temporal fields.
J. Comput. Phys., 2021

BoA-PTA, A Bayesian Optimization Accelerated Error-Free SPICE Solver.
CoRR, 2021

Residual Gaussian Process: A Tractable Nonparametric Bayesian Emulator for Multi-fidelity Simulations.
CoRR, 2021

Multi-Fidelity High-Order Gaussian Processes for Physical Simulation.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Physics Regularized Gaussian Processes.
CoRR, 2020

Scalable Variational Gaussian Process Regression Networks.
CoRR, 2020

Multi-Fidelity Bayesian Optimization via Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Scalable Gaussian Process Regression Networks.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Deep Coregionalization for the Emulation of Spatial-Temporal Fields.
CoRR, 2019

Data-Driven Model Order Reduction for Diffeomorphic Image Registration.
Proceedings of the Information Processing in Medical Imaging, 2019

Scalable High-Order Gaussian Process Regression.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017
Prediction of impurities in hydrogen fuel supplies using a thermally-modulated CMOS gas sensor: Experiments and modelling.
Proceedings of the 2017 IEEE SENSORS, Glasgow, United Kingdom, October 29, 2017

2016
Manifold learning for the emulation of spatial fields from computational models.
J. Comput. Phys., 2016


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