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BOiLS: Bayesian Optimisation for Logic Synthesis

Nov 11, 2021
Antoine Grosnit, Cedric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou Ammar

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Approximate Neural Architecture Search via Operation Distribution Learning

Nov 08, 2021
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Fabio M. Carlucci

* WACV 2022. 10 pages, 3 figures and 5 tables (15 pages, 7 figures and 6 tables including appendices) 

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Adversarial Attacks on Graph Classification via Bayesian Optimisation

Nov 04, 2021
Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong

* NeurIPS 2021. 11 pages, 8 figures, 2 tables (24 pages, 17 figures, 8 tables including references and appendices) 

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Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces

Feb 14, 2021
Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael A. Osborne

* 9 page, 6 figures (26 pages, 13 figures, 2 tables including references and appendices) 

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Explaining the Adaptive Generalisation Gap

Nov 15, 2020
Diego Granziol, Samuel Albanie, Xingchen Wan, Stephen Roberts

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Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel

Jun 13, 2020
Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne

* 8 pages, 4 figures (21 pages, 13 figures including references and appendices) 

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Iterate Averaging Helps: An Alternative Perspective in Deep Learning

Mar 02, 2020
Diego Granziol, Xingchen Wan, Stephen Roberts

* 9 pages, 8 figures, 21 pages including references and appendix 

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MLRG Deep Curvature

Dec 20, 2019
Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry Vetrov, Stephen Roberts

* 11 pages, 11 figures 

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