TY - GEN
T1 - Batch Training of Gaussian Process for Up-sampling Problems in S-Parameter Predictions
AU - Guo, Yiliang
AU - Li, Xingchen
AU - Wang, Yifan
AU - Kumar, Rahul
AU - Swaminathan, Madhavan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2018
Y1 - 2018
N2 - In using Machine Learning (ML) methods to predict S-parameters, handling the dimensionality problem of mapping the low-dimension design parameters to high-dimension responses is important. We propose to use batch training of Gaussian Process (GP) to map the design parameters into latent Gaussian space instead of linear mappings to create the non-linearity property as well as avoiding the saturation of activation functions before applying transposed kernels. Results show that the proposed model achieves better performance with regard to loss and normalized mean-squared error.
AB - In using Machine Learning (ML) methods to predict S-parameters, handling the dimensionality problem of mapping the low-dimension design parameters to high-dimension responses is important. We propose to use batch training of Gaussian Process (GP) to map the design parameters into latent Gaussian space instead of linear mappings to create the non-linearity property as well as avoiding the saturation of activation functions before applying transposed kernels. Results show that the proposed model achieves better performance with regard to loss and normalized mean-squared error.
UR - http://www.scopus.com/inward/record.url?scp=85179619334&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179619334&partnerID=8YFLogxK
U2 - 10.1109/EPEPS58208.2023.10314896
DO - 10.1109/EPEPS58208.2023.10314896
M3 - Conference contribution
AN - SCOPUS:85179619334
T3 - EPEPS 2023 - IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems
BT - EPEPS 2023 - IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2023
Y2 - 15 October 2023 through 18 October 2023
ER -