乐胖代购免代理版

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Yu , \" Bridging the gap between layout pattern sampling and hotspot detection via batch active sampling,\" arXiv preprint arXiv:1807.06446 , 2018 . H. Yang, S. Li, C. Tabery, B. Lin, and B. Yu, \"Bridging the gap between layout pattern sampling and hotspot detection via batch active sampling,\" arXiv preprint arXiv:1807.06446, 2018."},{"volume-title":"Data efficient lithography modeling with transfer learning and active data selection,\" IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","year":"2018","author":"Lin Y.","key":"e_1_3_2_1_10_1","unstructured":"Y. Lin , M. Li , Y. Watanabe , T. Kimura , T. Matsunawa , S. Nojima , and D. Z. Pan , \" Data efficient lithography modeling with transfer learning and active data selection,\" IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2018 . Y. Lin, M. Li, Y. Watanabe, T. Kimura, T. Matsunawa, S. Nojima, and D. Z. Pan, \"Data efficient lithography modeling with transfer learning and active data selection,\" IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018."},{"volume-title":"ICSICT","year":"2016","author":"Zhuang L.","key":"e_1_3_2_1_11_1","unstructured":"L. Zhuang , J. Xu , M. Tsai , Q. W. Liu , E. Yang , Y. Zhang , J. Sweis , C. Lai , and H. Ding , \" A novel methodology of process weak-point identification to accelerate process development and yield ramp-up,\" in Proc . ICSICT , 2016 . L. Zhuang, J. Xu, M. Tsai, Q. W. Liu, E. Yang, Y. Zhang, J. Sweis, C. Lai, and H. Ding, \"A novel methodology of process weak-point identification to accelerate process development and yield ramp-up,\" in Proc. ICSICT, 2016."},{"volume-title":"NIPS","year":"2014","author":"Goodfellow I.","key":"e_1_3_2_1_12_1","unstructured":"I. Goodfellow , J. Pouget-Abadie , M. Mirza , B. Xu , D. Warde-Farley , S. Ozair , A. Courville , and Y. 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