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Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems.
No free lunch theorems for optimization
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Collective classification in network data
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The fifth pascal recognizing textual entailment challenge
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Adam: A method for stochastic optimization
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The Monte Carlo method: the method of statistical trials
Yu A Shreider · 2014
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Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
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Deep residual learning for image recognition
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Ke Li and Jitendra Malik · 2016
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Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V Le · 2017
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Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
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Learning to learn without gradient descent by gradient descent
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Inductive representation learning on large graphs
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Barret Zoph and Quoc V. Le · 2017
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
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Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 2019
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
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Open graph benchmark: Datasets for machine learning on graphs
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Nas-fpn: Learning scalable feature pyramid architecture for object detection
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Yufan Jiang, Chi Hu, Tong Xiao, Chunliang Zhang, and Jingbo Zhu · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Guillaume Lample and François Charton · 2019
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Esteban Real, Chen Liang, David So, and Quoc Le · 2020
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Learning differentiable programs with admissible neural heuristics
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Ai feynman: A physics-inspired method for symbolic regression
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Rethinking architecture selection in differentiable nas
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Fast is better than free: Revisiting adversarial training
Eric Wong, Leslie Rice, and J Zico Kolter · 2020
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Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
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Learning to optimize: A primer and a benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, and Wotao Yin · 2021
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Differentiable synthesis of program architectures
Guofeng Cui and He Zhu · 2021
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Generalizing few-shot nas with gradient matching
Shoukang Hu, Ruochen Wang, HONG Lanqing, Zhenguo Li, Cho-Jui Hsieh, and Jiashi Feng · 2021
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Discovering symbolic policies with deep reinforcement learning
Mikel Landajuela, Brenden K Petersen, Sookyung Kim, Claudio P Santiago, Ruben Glatt, Nathan Mundhenk, Jacob F Pettit, and Daniel Faissol · 2021
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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K Petersen, Mikel Landajuela, T Nathan Mundhenk, Claudio P Santiago, Soo K Kim, and Joanne T Kim · 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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Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies
Paul Vicol, Luke Metz, and Jascha Sohl-Dickstein · 2021
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Symbolic learning to optimize: Towards interpretability and scalability
Wenqing Zheng, Tianlong Chen, Ting-Kuei Hu, and Zhangyang Wang · 2021
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Evolved optimizer for visio
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