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In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning.
Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 1956
Earlier work this paper cites.
On bayesian methods for seeking the extremum
Jonas Močkus · 1975
Earlier work this paper cites.
Evolutionary principles in self-referential learning. on learning how to learn: The meta-meta-meta…-hook
Jurgen Schmidhuber · 1987
Earlier work this paper cites.
Self organizing neural networks for the identification problem
Manoel Tenorio and Wei-Tsih Lee · 1988
Earlier work this paper cites.
Designing neural networks using genetic algorithms
Geoffrey F Miller, Peter M Todd, and Shailesh U Hegde · 1989
Earlier work this paper cites.
Designing neural networks using genetic algorithms with graph generation system
Hiroaki Kitano · 1990
Earlier work this paper cites.
A comparative analysis of selection schemes used in genetic algorithms
David E Goldberg and Kalyanmoy Deb · 1991
Earlier work this paper cites.
A statistical method for global optimization
Dennis D Cox and Susan John · 1992
Earlier work this paper cites.
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Jürgen Schmidhuber · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Timit acoustic phonetic continuous speech corpus
John S Garofolo · 1993
Earlier work this paper cites.
A ‘self-referential’weight matrix
Jürgen Schmidhuber · 1993
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.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K Warmuth · 1997
Earlier work this paper cites.
Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces
Rainer Storn and Kenneth Price · 1997
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 1998
Earlier work this paper cites.
Object recognition with gradient-based learning
Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio · 1999
Earlier work this paper cites.
Learning to learn using gradient descent
Sepp Hochreiter, A. Steven Younger, and Peter R. Conwell · 2001
Earlier work this paper cites.
Deeper insights into weight sharing in neural architecture search
Yuge Zhang, Zejun Lin, Junyang Jiang, Quanlu Zhang, Yujing Wang, Hui Xue, Chen Zhang, and Yaming Yang · 2001
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
Earlier work this paper cites.
Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2003
Earlier work this paper cites.
Probabilistic dual network architecture search on graphs
Yiren Zhao, Duo Wang, Xitong Gao, Robert Mullins, Pietro Lio, and Mateja Jamnik · 2003
Earlier work this paper cites.
Neural architecture optimization with graph vae
Jian Li, Yong Liu, Jiankun Liu, and Weiping Wang · 2006
Earlier work this paper cites.
Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
Earlier work this paper cites.
Simplifying architecture search for graph neural network
Huan Zhao, Lanning Wei, and Quanming Yao · 2008
Earlier work this paper cites.
Darts-: robustly stepping out of performance collapse without indicators
Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, and Junchi Yan · 2009
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2010
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2011
Earlier work this paper cites.
A survey of monte carlo tree search methods
Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton · 2012
Earlier work this paper cites.
Entropy search for information-efficient global optimization
Philipp Hennig and Christian J Schuler · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 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.
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep speech: Scaling up end-to-end speech recognition
Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, et al · 2014
Earlier work this paper cites.
Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W Hoffman, and Zoubin Ghahramani · 2014
Earlier work this paper cites.
Raiders of the lost architecture: Kernels for bayesian optimization in conditional parameter spaces
Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, and Michael A. Osborne · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Attention-based models for speech recognition
Jan K Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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.
Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. T. Springenberg, M. Blum, and F. Hutter · 2015
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals · 2016
Earlier work this paper cites.
Net2net: Accelerating learning via knowledge transfer
Tianqi Chen, Ian J. Goodfellow, and Jonathon Shlens · 2016
Earlier work this paper cites.
Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
Earlier work this paper cites.
A survey on stereo matching techniques for 3d vision in image processing
Deepika Kumari and Kamaljit Kaur · 2016
Earlier work this paper cites.
Scalable gradient-based tuning of continuous regularization hyperparameters
Jelena Luketina, Mathias Berglund, Klaus Greff, and Tapani Raiko · 2016
Earlier work this paper cites.
Towards automatically-tuned neural networks
H Mendoza, A Klein, M Feurer, J Springenberg, and F Hutter · 2016
Earlier work this paper cites.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
R. Olson, N. Bartley, R. Urbanowicz, and J. Moore · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Bayesian optimization with robust bayesian neural networks
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, and Frank Hutter · 2016
Earlier work this paper cites.
Network morphism
Tao Wei, Changhu Wang, Yong Rui, and Chang Wen Chen · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
Earlier work this paper cites.
Simple and efficient architecture search for convolutional neural networks
Thomas Elsken, Jan-Hendrik Metzen, and Frank Hutter · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Deep convolutional networks for human sketches by means of the evolutionary deep learning
Saya Fujino, Naoki Mori, and Keinosuke Matsumoto · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Earlier work this paper cites.
Hypernetworks
David Ha, Andrew Dai, and Quoc V. Le · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
Multi-fidelity Bayesian optimisation with continuous approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, and Barnabás Póczos · 2017
Earlier work this paper cites.
Learning curve prediction with bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2017
Earlier work this paper cites.
David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, and Aaron Courville · 2017
Earlier work this paper cites.
Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
Deeparchitect: Automatically designing and training deep architectures
Renato Negrinho and Geoff Gordon · 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 V. Le, and Alexey Kurakin · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Earlier work this paper cites.
A genetic programming approach to designing convolutional neural network architectures
Masanori Suganuma, Shinichi Shirakawa, and Tomoharu Nagao · 2017
Earlier work this paper cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Max-value entropy search for efficient bayesian optimization
Zi Wang and Stefanie Jegelka · 2017
Earlier work this paper cites.
Genetic cnn
Lingxi Xie and Alan Yuille · 2017
Earlier work this paper cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Earlier work this paper cites.
Evolutionary deep learning-based energy consumption prediction for buildings
Abdulaziz Almalaq and Jun Jason Zhang · 2018
Earlier work this paper cites.
Accelerating neural architecture search using performance prediction
Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2018
Earlier work this paper cites.
Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
Cited alongside, same era.
Smash: One-shot model architecture search through hypernetworks
Andrew Brock, Theo Lim, JM Ritchie, and Nick Weston · 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.
Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
A tutorial on bayesian optimization
Peter I Frazier · 2018
Cited alongside, same era.
A genetic algorithm for convolutional network structure optimization for concrete crack detection
Automl-zero: Evolving machine learning algorithms from scratch
Esteban Real, Chen Liang, David So, and Quoc Le · 2020
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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
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Naslib: a modular and flexible neural architecture search library, 2020
Michael Ruchte, Arber Zela, Julien Siems, Josif Grabocka, and Frank Hutter · 2020
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Automated design of error-resilient and hardware-efficient deep neural networks
Christoph Schorn, Thomas Elsken, Sebastian Vogel, Armin Runge, Andre Guntoro, and Gerd Ascheid · 2020
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2020
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Spencer Gibb, Hung Manh La, and Sushil Louis · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 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.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Cited alongside, same era.
Alphagan: Generative adversarial networks for natural image matting
Sebastian Lutz, Konstantinos Amplianitis, and Aljoscha Smolic · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
Cited alongside, same era.
Bridging the gap between sample-based and one-shot neural architecture search with bonas
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, and Tong Zhang · 2020
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Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2020
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Efficient residual dense block search for image super-resolution
Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, and Yunhe Wang · 2020
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Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune · 2020
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Automatically designing cnn architectures using the genetic algorithm for image classification
Yanan Sun, Bing Xue, Mengjie Zhang, Gary G Yen, and Jiancheng Lv · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
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Off-policy reinforcement learning for efficient and effective gan architecture search
Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, and Olga Fink · 2020
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Neural architecture search, 2020
Lilian Weng · 2020
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A study on encodings for neural architecture search
Colin White, Willie Neiswanger, Sam Nolen, and Yash Savani · 2020
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Does unsupervised architecture representation learning help neural architecture search?
Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, and Mi Zhang · 2020
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Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 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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How to train your super-net: An analysis of training heuristics in weight-sharing nas
Kaicheng Yu, Rene Ranftl, and Mathieu Salzmann · 2020
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Hyper-parameter optimization: A review of algorithms and applications
Tong Yu and Hong Zhu · 2020
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Econas: Finding proxies for economical neural architecture search
Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang, Chen Change Loy, Shuai Yi, Xuesen Zhang, and Wanli Ouyang · 2020
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Zero-cost proxies for lightweight {nas}
Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas Donald Lane · 2021
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A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar, Martin Wistuba, and Naigang Wang · 2021
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A novel evolutionary algorithm for hierarchical neural architecture search
Aristeidis Chrostoforidis, George Kyriakides, and Konstantinos Margaritis · 2021
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Fbnetv3: Joint architecture-recipe search using predictor pretraining
Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, et al · 2021
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Literature list on Neural Architecture Search, 2021
Difan Deng and Marius Lindauer · 2021
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Degas: differentiable efficient generator search
Sivan Doveh and Raja Giryes · 2021
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Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search
Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, and Zhenguo Li · 2021
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Automating generative adversarial networks using neural architecture search: A review
Vayangi Vishmi Vishara Ganepola and Torin Wirasingha · 2021
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Nasvit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training
Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Vikas Chandra, et al · 2021
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Automl: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2021
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Meta-learning in neural networks: A survey
T. M. Hospedales, A. Antoniou, P. Micaelli, and A. J. Storkey · 2021
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Bag of baselines for multi-objective joint neural architecture search and hyperparameter optimization
Sergio Izquierdo, Julia Guerrero-Viu, Sven Hauns, Guilherme Miotto, Simon Schrodi, André Biedenkapp, Thomas Elsken, Difan Deng, Marius Lindauer, and Frank Hutter · 2021
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Regularization is all you need: Simple neural nets can excel on tabular data
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
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Reduced, reused and recycled: The life of a dataset in machine learning research
Bernard Koch, Emily Denton, Alex Hanna, and Jacob G Foster · 2021
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Rapid neural architecture search by learning to generate graphs from datasets
Hayeon Lee, Eunyoung Hyung, and Sung Ju Hwang · 2021
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Zen-nas: A zero-shot nas for high-performance image recognition
Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin · 2021
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Smooth variational graph embeddings for efficient neural architecture search
Jovita Lukasik, David Friede, Arber Zela, Frank Hutter, and Margret Keuper · 2021
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Dehb: Evolutionary hyperband for scalable, robust and efficient hyperparameter optimization
Neeratyoy Mallik and Noor Awad · 2021
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Nas-bench-asr: Reproducible neural architecture search for speech recognition
Abhinav Mehrotra, Alberto Gil C. P. Ramos, Sourav Bhattacharya, Łukasz Dudziak, Ravichander Vipperla, Thomas Chau, Mohamed S Abdelfattah, Samin Ishtiaq, and Nicholas Donald Lane · 2021
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Neural architecture search without training
Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley · 2021
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Neural Network Intelligence, 2021
Microsoft · 2021
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Accelerating neural architecture search via proxy data
Byunggook Na, Jisoo Mok, Hyeokjun Choe, and Sungroh Yoon · 2021
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Multi-headed neural ensemble search
Ashwin Raaghav Narayanan, Arber Zela, Tonmoy Saikia, Thomas Brox, and Frank Hutter · 2021
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Learning the pareto front with hypernetworks
Aviv Navon, Aviv Shamsian, Gal Chechik, and Ethan Fetaya · 2021
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Optimal transport kernels for sequential and parallel neural architecture search
Vu Nguyen, Tam Le, Makoto Yamada, and Michael A Osborne · 2021
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Evaluating efficient performance estimators of neural architectures
Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, and Yu Wang · 2021
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Local search is a remarkably strong baseline for neural architecture search
T Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, and Peter AN Bosman · 2021
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Ai and the everything in the whole wide world benchmark
Inioluwa Deborah Raji, Emily M Bender, Amandalynne Paullada, Emily Denton, and Alex Hanna · 2021
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Rethinking neural operations for diverse tasks
Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, and Ameet Talwalkar · 2021
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Neural architecture search using bayesian optimisation with weisfeiler-lehman kernel
Binxin Ru, Xingchen Wan, Xiaowen Dong, and Michael Osborne · 2021
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Gradient descent effects on differential neural architecture search: A survey
Santanu Santra, Jun-Wei Hsieh, and Chi-Fang Lin · 2021
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Mutation is all you need
Lennart Schneider, Florian Pfisterer, Martin Binder, and Bernd Bischl · 2021
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Proxybo: Accelerating neural architecture search via bayesian optimization with zero-cost proxies
Yu Shen, Yang Li, Jian Zheng, Wentao Zhang, Peng Yao, Jixiang Li, Sen Yang, Ji Liu, and Cui Bin · 2021
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Core-set sampling for efficient neural architecture search
Jae-hun Shim, Kyeongbo Kong, and Suk-Ju Kang · 2021
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Nasi: Label-and data-agnostic neural architecture search at initialization
Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, and Bryan Kian Hsiang Low · 2021
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Primer: Searching for efficient transformers for language modeling, 2021
David R. So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, and Quoc V. Le · 2021
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Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
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Vitas: Vision transformer architecture search
Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Rethinking architecture selection in differentiable nas
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
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A deeper look at zero-cost proxies for lightweight nas
Colin White, Mikhail Khodak, Renbo Tu, Shital Shah, Sébastien Bubeck, and Dey Debadeepta · 2021
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Trilevel neural architecture search for efficient single image super-resolution
Yan Wu, Zhiwu Huang, Suryansh Kumar, Rhea Sanjay Sukthanker, Radu Timofte, and Luc Van Gool · 2021
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Zero-cost proxies meet differentiable architecture search
Lichuan Xiang, Łukasz Dudziak, Mohamed S Abdelfattah, Thomas Chau, Nicholas D Lane, and Hongkai Wen · 2021
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Weight-sharing neural architecture search: A battle to shrink the optimization gap
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Bayesian Optimization
Roman Garnett · 2023
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