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Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and Andrei A Lehman · 1968
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The application of bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas · 1978
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Hardness of approximating graph transformation problem
Chih-Long Lin · 1994
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Determining the significance of input parameters using sensitivity analysis
Andries Petrus Engelbrecht, Ian Cloete, and Jacek M Zurada · 1995
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Marginalized kernels between labeled graphs
Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi · 2003
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On graph kernels: Hardness results and efficient alternatives
Thomas Gärtner, Peter Flach, and Stefan Wrobel · 2003
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Shortest-path kernels on graphs
Karsten M Borgwardt and Hans-Peter Kriegel · 2005
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Gaussian processes for machine learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Comparing stars: On approximating graph edit distance
Zhiping Zeng, Anthony KH Tung, Jianyong Wang, Jianhua Feng, and Lizhu Zhou · 2009
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Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Eric Brochu, Vlad M Cora, and Nando De Freitas · 2010
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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Multiple kernel learning algorithms
Mehmet Gönen and Ethem Alpaydin · 2011
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Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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The multiscale laplacian graph kernel
Risi Kondor and Horace Pan · 2016
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On valid optimal assignment kernels and applications to graph classification
Nils M Kriege, Pierre-Louis Giscard, and Richard Wilson · 2016
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc Le · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
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Multi-objective neural architecture search via predictive network performance optimization, 2019
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T Kwok, and Tong Zhang · 2019
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Exploring randomly wired neural networks for image recognition
Saining Xie, Alexander Kirillov, Ross Girshick, and Kaiming He · 2019
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Giannis Nikolentzos, Giannis Siglidis, and Michalis Vazirgiannis · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
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Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2018
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Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
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Sample-efficient neural architecture search by learning action space
Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, and Yuandong Tian · 2019
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Understanding architectures learnt by cell-based neural architecture search
Yao Shu, Wei Wang, and Shaofeng Cai · 2019
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Combinatorial bayesian optimization using the graph cartesian product
Changyong Oh, Jakub Tomczak, Efstratios Gavves, and Max Welling · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Darts+: Improved differentiable architecture search with early stopping
Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, and Zhenguo Li · 2019
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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
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DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Asynchronous batch bayesian optimisation with improved local penalisation
Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, and Michael A Osborne · 2019
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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
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A survey on graph kernels
Nils M Kriege, Fredrik D Johansson, and Christopher Morris · 2020
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Enriched weisfeiler-lehman kernel for improved graph clustering of source code
Frank Höppner and Maximilian Jahnke · 2020
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Graph structure of neural networks
Jiaxuan You, J. Leskovec, Kaiming He, and Saining Xie · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Gp-nas: Gaussian process based neural architecture search
Zhihang Li, Teng Xi, Jiankang Deng, Gang Zhang, Shengzhao Wen, and Ran He · 2020
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