Fetching the paper…
Reading the bibliography…
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks.
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan Yuille, and Li Fei-Fei · 1901
Earlier work this paper cites.
DetNAS: Neural Architecture Search on Object Detection
Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Chunhong Pan, and Jian Sun · 1903
Earlier work this paper cites.
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang, and Quoc V. Le · 1904
Earlier work this paper cites.
Single Path One-Shot Neural Architecture Search with Uniform Sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun · 1904
Earlier work this paper cites.
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Ruijun Xu, and Jixiang Li · 1907
Earlier work this paper cites.
A new measure of rank correlation
Maurice G Kendall · 1938
Earlier work this paper cites.
The generalization ofstudent’s’ problem when several different population variances are involved
Bernard L Welch · 1947
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Cifar-10 (canadian institute for advanced research)
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
Earlier work this paper cites.
Improved Regularization of Convolutional Neural Networks with Cutout
Terrance DeVries and Graham W. Taylor · 2017
Earlier work this paper cites.
Regularizing and optimizing lstm language models
Stephen Merity, Nitish Shirish Keskar, and Richard Socher · 2017
Earlier work this paper cites.
Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, and Alex Kurakin · 2017
Cited alongside, same era.
Genetic cnn
L. Xie and A. Yuille · 2017
Cited alongside, same era.
Breaking the softmax bottleneck: A high-rank rnn language model
Zhilin Yang, Zihang Dai, Ruslan Salakhutdinov, and William W Cohen · 2017
Cited alongside, same era.
Neural Architecture Search with Reinforcement Learning
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
Maskconnect: Connectivity learning by gradient descent
Karim Ahmed and Lorenzo Torresani · 2018
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2018
Later among the works it cites.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
Later among the works it cites.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Later among the works it cites.
Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2019
Closest in time.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
Cited alongside, same era.
Searching for efficient multi-scale architectures for dense image prediction
Liang-Chieh Chen, Maxwell Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, and Jon Shlens · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing · 2018
Cited alongside, same era.
Nsga-net: A multi-objective genetic algorithm for neural architecture search
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf · 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.
Efficient progressive neural architecture search
Juan-Manuel Pérez-Rúa, Moez Baccouche, and Stephane Pateux · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
Cited alongside, same era.
Evolving deep neural networks
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, et al · 2019
Closest in time.
On Network Design Spaces for Visual Recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 2019
Closest in time.
The evolved transformer
David R. So, Chen Liang, and Quoc V. Le · 2019
Closest in time.
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, and Rodrigo Fonseca · 2019
Closest in time.
Exploring randomly wired neural networks for image recognition
Saining Xie, Alexander Kirillov, Ross Girshick, and Kaiming He · 2019
Closest in time.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
Closest in time.
Bayesnas: A bayesian approach for neural architecture search
Hongpeng Zhou, Minghao Yang, Jun Wang, and Wei Pan · 2019
Closest in time.