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Architecture search is the process of automatically learning the neural model or cell structure that best suits the given task.
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Human-level concept learning through probabilistic program induction
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Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2015 · 2015
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Arvind Neelakantan, Quoc V Le, and Ilya Sutskever. 2015 · 2015
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CIDEr: Consensus-based image description evaluation
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Learning transferable architectures for scalable image recognition
Progressive neural architecture search
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Deeparchitect: Automatically designing and training deep architectures
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Variational continual learning
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Zero-shot task generalization with multi-task deep reinforcement learning
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Sluice networks: Learning what to share between loosely related tasks
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Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. 2018 · 2015
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Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. 2016 · 2016
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Lifelong machine learning
Zhiyuan Chen and Bing Liu. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Less-forgetting learning in deep neural networks
Heechul Jung, Jeongwoo Ju, Minju Jung, and Junmo Kim. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
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Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2017 · 2017
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Learning to select data for transfer learning with bayesian optimization
Sebastian Ruder and Barbara Plank. 2017 · 2017
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Group sparse regularization for deep neural networks
Simone Scardapane, Danilo Comminiello, Amir Hussain, and Aurelio Uncini. 2017 · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017 · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017 · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le. 2017 · 2017
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Multi-task learning of pairwise sequence classification tasks over disparate label spaces
Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018 · 2018
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang. 2018 · 2018
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Soft layer-specific multi-task summarization with entailment and question generation
Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 2018 · 2018
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Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. 2018 · 2018
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Deep contextualized word representations
Mat thew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang. 2018 · 2018
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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019 · 2019
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