Fetching the paper…
Reading the bibliography…
Monumental advances in deep learning have led to unprecedented achievements across various domains.
Designing neural networks using genetic algorithms
Geoffrey F. Miller, Peter M. Todd, and Shailesh U. Hegde · 1989
Earlier work this paper cites.
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
John H. Holland · 1992
Earlier work this paper cites.
An evolutionary algorithm that constructs recurrent neural networks
Peter J. Angeline, Gregory M. Saunders, and Jordan B. Pollack · 1994
Earlier work this paper cites.
WordNet: a lexical database for English
George A Miller · 1995
Earlier work this paper cites.
Multiobjective optimization using evolutionary algorithms - A comparative case study
Eckart Zitzler and Lothar Thiele · 1998
Earlier work this paper cites.
A fast and elitist multiobjective genetic algorithm: NSGA-II
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan · 2002
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
Earlier work this paper cites.
Survey of multi-objective optimization methods for engineering
R. T. Marler and J. S. Arora · 2004
Earlier work this paper cites.
Reference point based multi-objective optimization using evolutionary algorithms
Kalyanmoy Deb and J. Sundar · 2006
Earlier work this paper cites.
An improved dimension-sweep algorithm for the hypervolume indicator
Carlos M. Fonseca, Luís Paquete, and Manuel López-Ibáñez · 2006
Earlier work this paper cites.
Self-adaptive simulated binary crossover for real-parameter optimization
Kalyanmoy Deb, Karthik Sindhya, and Tatsuya Okabe · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David D. Cox · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter · 2015
Earlier work this paper cites.
Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Mai Nguyen, Thomas J. Fuchs, and Hod Lipson · 2015
Earlier work this paper cites.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Towards automatically-tuned neural networks
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter · 2016
Cited alongside, same era.
A downsampled variant of imagenet as an alternative to the CIFAR datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2018
Later among the works it cites.
Understanding neural architecture search techniques
George Adam and Jonathan Lorraine · 2019
Later among the works it cites.
Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
Later among the works it cites.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
Later among the works it cites.
Darpa’s explainable artificial intelligence (xai) program
David Gunning · 2019
Later among the works it cites.
DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
Deephyper: Asynchronous hyperparameter search for deep neural networks
Prasanna Balaprakash, Michael A. Salim, Thomas D. Uram, Venkat Vishwanath, and Stefan M. Wild · 2018
Cited alongside, same era.
Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
Cited alongside, same era.
Later among the works it cites.
NSGA-Net: neural architecture search using multi-objective genetic algorithm
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh D. Dhebar, Kalyanmoy Deb, Erik D. Goodman, and Wolfgang Banzhaf · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
Later among the works it cites.
Why are we using black box models in ai when we don’t need to? a lesson from an explainable ai competition
Cynthia Rudin and Joanna Radin · 2019
Later among the works it cites.
Understanding the role of individual units in a deep neural network
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Àgata Lapedriza, Bolei Zhou, and Antonio Torralba · 2020
Later among the works it cites.
pymoo: Multi-objective optimization in python
J. Blank and K. Deb · 2020
Later among the works it cites.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Later among the works it cites.
Neuron shapley: Discovering the responsible neurons
Amirata Ghorbani and James Y. Zou · 2020
Later among the works it cites.
Disentangled neural architecture search
Xinyue Zheng, Peng Wang, Qigang Wang, and Zhongchao Shi · 2020
Later among the works it cites.
Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels
Bin Xin Ru, Xingchen Wan, Xiaowen Dong, and Michael A. Osborne · 2021
Closest in time.
Modularized morphing of deep convolutional neural networks: A graph approach
Tao Wei, Changhu Wang, and Chang Wen Chen · 2021
Closest in time.