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Automated machine learning (AutoML) usually involves several crucial components, such as Data Augmentation (DA) policy, Hyper-Parameter Optimization (HPO), and Neural Architecture Search (NAS).
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
Ingrid Daubechies, Michel Defrise, and Christine De Mol · 2004
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What, where and who? classifying events by scene and object recognition
Li-Jia Li and Li Fei-Fei · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Recognizing indoor scenes
Ariadna Quattoni and Antonio Torralba · 2009
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Robustness and generalization
Huan Xu and Shie Mannor · 2012
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Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2013
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Batch Bayesian optimization via local penalization
Javier González, Zhenwen Dai, Philipp Hennig, and Neil D Lawrence · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Batched Gaussian process bandit optimization via determinantal point processes
Tarun Kathuria, Amit Deshpande, and Pushmeet Kohli · 2016
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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
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Multi-fidelity Bayesian optimisation with continuous approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, and Barnabás Póczos · 2017
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2017
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing · 2018
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Scalable hyperparameter transfer learning
Valerio Perrone, Rodolphe Jenatton, Matthias Seeger, and Cédric Archambeau · 2018
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Efficient neural architecture search via parameters 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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MobilenetV2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
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Asynchronous batch bayesian optimisation with improved local penalisation
Ahsan S Alvi, Binxin Ru, Jan Calliess, Stephen J Roberts, and Michael A Osborne · 2019
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Bayesnas: A bayesian approach for neural architecture search
Hongpeng Zhou, Minghao Yang, Jun Wang, and Wei Pan · 2019
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc Le · 2019
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Once-for-all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2020
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Randaugment: practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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FBNetV3: Joint architecture-recipe search using neural acquisition function
Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, et al · 2020
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Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
Cited alongside, same era.
Manas: Multi-agent neural architecture search
Fabio Maria Carlucci, Pedro M Esperança, Marco Singh, Victor Gabillon, Antoine Yang, Hang Xu, Zewei Chen, and Jun Wang · 2019
Cited alongside, same era.
Probabilistic neural architecture search
Francesco Paolo Casale, Jonathan Gordon, and Nicolo Fusi · 2019
Cited alongside, same era.
Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
Cited alongside, same era.
Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
Cited alongside, same era.
Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Xuanyi Dong, Mingxing Tan, Adams Wei Yu, Daiyi Peng, Bogdan Gabrys, and Quoc V Le · 2020
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DSNAS: Direct neural architecture search without parameter retraining
Shoukang Hu, Sirui Xie, Hehui Zheng, Chunxiao Liu, Jianping Shi, Xunying Liu, and Dahua Lin · 2020
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Joint search of data augmentation policies and network architectures
Taiga Kashima, Yoshihiro Yamada, and Shunta Saito · 2020
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Rethinking the hyperparameters for fine-tuning
Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto · 2020
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Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
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Generalizing transfer bayesian optimization to source-target heterogeneity
Alan Tan Wei Min, Abhishek Gupta, and Yew-Soon Ong · 2020
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HyperSTAR: Task-aware hyperparameters for deep networks
Gaurav Mittal, Chang Liu, Nikolaos Karianakis, Victor Fragoso, Mei Chen, and Yun Fu · 2020
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Onlineaugment: Online data augmentation with less domain knowledge
Zhiqiang Tang, Yunhe Gao, Leonid Karlinsky, Prasanna Sattigeri, Rogerio Feris, and Dimitris Metaxas · 2020
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APQ: Joint search for network architecture, pruning and quantization policy
Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, and Song Han · 2020
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Circumventing outliers of autoaugment with knowledge distillation
Longhui Wei, An Xiao, Lingxi Xie, Xin Chen, Xiaopeng Zhang, and Qi Tian · 2020
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Pc-darts: Partial channel connections for memory-efficient architecture search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong · 2020
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Ista-nas: Efficient and consistent neural architecture search by sparse coding
Yibo Yang, Hongyang Li, Shan You, Fei Wang, Chen Qian, and Zhouchen Lin · 2020
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Sm-nas: structural-to-modular neural architecture search for object detection
Lewei Yao, Hang Xu, Wei Zhang, Xiaodan Liang, and Zhenguo Li · 2020
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Adversarial autoaugment
Xinyu Zhang, Qiang Wang, Jian Zhang, and Zhao Zhong · 2020
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DrNAS: Dirichlet neural architecture search
Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
Closest in time.
Online hyperparameter optimization by real-time recurrent learning
Daniel Jiwoong Im, Cristina Savin, and Kyunghyun Cho · 2021
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Geometry-aware gradient algorithms for neural architecture search
Liam Li, Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2021
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Metaaugment: Sample-aware data augmentation policy learning
Fengwei Zhou, Jiawei Li, Chuanlong Xie, Fei Chen, Lanqing Hong, Rui Sun, and Zhenguo Li · 2021
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