Fetching the paper…
Reading the bibliography…
Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem.
Flat minima
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based optimization of hyperparameters
Y. Bengio · 2000
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
Earlier work this paper cites.
An overview of bilevel optimization, 2007
Benoît Colson, Patrice Marcotte, and Gilles Savard · 2007
Earlier work this paper cites.
A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
Earlier work this paper cites.
Generic methods for optimization-based modeling
Justin Domke · 2012
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
Earlier work this paper cites.
Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
Earlier work this paper cites.
On large-batch training for deep learning: Generalization gap and sharp minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang · 2016
Earlier work this paper cites.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
Earlier work this paper cites.
Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
Earlier work this paper cites.
Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
Earlier work this paper cites.
On optimal generalizability in parametric learning
Ahmad Beirami, Meisam Razaviyayn, Shahin Shahrampour, and Vahid Tarokh · 2017
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
Cited alongside, same era.
Simple And Efficient Architecture Search for Convolutional Neural Networks
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2017
Cited alongside, same era.
Forward and reverse gradient-based hyperparameter optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
Cited alongside, same era.
Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
Cited alongside, same era.
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 2017
Cited alongside, same era.
Hessian-based analysis of large batch training and robustness to adversaries
Zhewei Yao, Amir Gholami, Qi Lei, Kurt Keutzer, and Michael W Mahoney · 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.
Practical block-wise neural network architecture generation
Zhao Zhong, Jingchen Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu · 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.
ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
Closest in time.
Probabilistic neural architecture search
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 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.
Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
Cited alongside, same era.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C.Fernando, and K. Kavukcuoglu · 2018
Cited alongside, same era.
Stable bayesian optimization
Thanh Dai Nguyen, Sunil Gupta, Santu Rana, and Svetha Venkatesh · 2018
Cited alongside, same era.
Francesco Casale, Jonathan Gordon, and Nicolo Fusi · 2019
Closest in time.
Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Liu Chenxi, Chen Liang Chieh, Schroff Florian, Adam Hartwig, Hua Wei, Yuille Alan L., and Fei Fei Li · 2019
Closest in time.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
Closest in time.
DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Closest in time.
Aging Evolution for Image Classifier Architecture Search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
Closest in time.
Autodispnet: Improving disparity estimation with automl
T. Saikia, Y. Marrakchi, A. Zela, F. Hutter, and T. Brox · 2019
Closest in time.
Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
Closest in time.
SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
Closest in time.