Fetching the paper…
Reading the bibliography…
Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search bias.
On information and sufficiency
S. Kullback and R. A. Leibler · 1951
Earlier work this paper cites.
A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and AA Lehman · 1968
Earlier work this paper cites.
On bayesian methods for seeking the extremum and their application
Jonas Mockus · 1977
Earlier work this paper cites.
Creating artificial neural networks that generalize
Jocelyn Sietsma and Robert JF Dow · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
The effects of adding noise during backpropagation training on a generalization performance
Guozhong An · 1996
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, and Fu Jie Huang · 2006
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning convolutional feature hierarchies for visual recognition
Koray Kavukcuoglu, Pierre Sermanet, Y lan Boureau, Karol Gregor, Michael Mathieu, and Yann L. Cun · 2010
Earlier work this paper cites.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Active learning of linear embeddings for gaussian processes
Roman Garnett, Michael A. Osborne, and Philipp Hennig · 2014
Earlier work this paper cites.
Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederick P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow · 2016
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.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
NAS-Bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter · 2019
Later among the works it cites.
Deep Graph Infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
D-vae: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 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.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Grasp2vec: Learning object representations from self-supervised grasping
Eric Jang, Coline Devin, Vincent Vanhoucke, and Sergey Levine · 2018
Cited alongside, same era.
BOHB: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, En-Hong Chen, and Tie-Yan Liu · 2018
Cited alongside, same era.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Cited alongside, same era.
Bananas: Bayesian optimization with neural architectures for neural architecture search
Colin White, Willie Neiswanger, and Yash Savani · 2019
Later among the works it cites.
Efficient sample-based neural architecture search with learnable predictor
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, and Tong Zhang · 2019
Later among the works it cites.
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, and Pieter-Jan Kindermans · 2019
Later among the works it cites.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
Later among the works it cites.
On network design spaces for visual recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 2019
Later among the works it cites.
Milenas: Efficient neural architecture search via mixed-level reformulation
Chaoyang He, Haishan Ye, Li Shen, and Tong Zhang · 2020
Closest in time.
Understanding architectures learnt by cell-based neural architecture search
Yao Shu, Wei Wang, and Shaofeng Cai · 2020
Closest in time.
Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
Arber Zela, Julien Siems, and Frank Hutter · 2020
Closest in time.
Understanding and robustifying differentiable architecture search
Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, and Frank Hutter · 2020
Closest in time.
NAS-Bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
Graph structure of neural networks
Jiaxuan You, Jure Leskovec, Kaiming He, and Saining Xie · 2020
Closest in time.
From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf · 2020
Closest in time.
Graph-driven generative models for heterogeneous multi-task learning
Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, and Lawrence Carin · 2020
Closest in time.
A comprehensive survey of neural architecture search: Challenges and solutions
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang · 2020
Closest in time.
A study on encodings for neural architecture search
Colin White, Willie Neiswanger, Sam Nolen, and Yash Savani · 2020
Closest in time.
Are labels necessary for neural architecture search?
Chenxi Liu, Piotr Dollár, Kaiming He, Ross Girshick, Alan Yuille, and Saining Xie · 2020
Closest in time.
Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
Closest in time.