Learning to Generalize: Meta-Learning for Domain Generalization
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[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[3] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[4] Jürgen Schmidhuber,et al. Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.
[5] Masashi Sugiyama,et al. Mixture Regression for Covariate Shift , 2006, NIPS.
[6] Hal Daumé,et al. Frustratingly Easy Domain Adaptation , 2007, ACL.
[7] Peter Stone,et al. Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..
[8] Alexei A. Efros,et al. Undoing the Damage of Dataset Bias , 2012, ECCV.
[9] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[10] Bernhard Schölkopf,et al. Domain Generalization via Invariant Feature Representation , 2013, ICML.
[11] Jan Peters,et al. Data-Efficient Generalization of Robot Skills with Contextual Policy Search , 2013, AAAI.
[12] Dong Xu,et al. Exploiting Low-Rank Structure from Latent Domains for Domain Generalization , 2014, ECCV.
[13] Eric Eaton,et al. Online Multi-Task Learning for Policy Gradient Methods , 2014, ICML.
[14] Mengjie Zhang,et al. Domain Generalization for Object Recognition with Multi-task Autoencoders , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[15] Victor S. Lempitsky,et al. Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.
[16] Rama Chellappa,et al. Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.
[17] Eric Eaton,et al. Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment , 2015, AAAI.
[18] Yongxin Yang,et al. A Unified Perspective on Multi-Domain and Multi-Task Learning , 2014, ICLR.
[19] Eric Eaton,et al. Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning , 2015, IJCAI.
[20] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[21] George Trigeorgis,et al. Domain Separation Networks , 2016, NIPS.
[22] Ruslan Salakhutdinov,et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning , 2015, ICLR.
[23] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[24] Moshe Dor,et al. אבן, and: Stone , 2017 .
[25] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[26] Wojciech Zaremba,et al. OpenAI Gym , 2016, ArXiv.
[27] Andrea Vedaldi,et al. Universal representations: The missing link between faces, text, planktons, and cat breeds , 2017, ArXiv.
[28] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[29] Gabriela Csurka,et al. Domain Adaptation in Computer Vision Applications , 2017, Advances in Computer Vision and Pattern Recognition.
[30] Sergey Levine,et al. Generalizing Skills with Semi-Supervised Reinforcement Learning , 2016, ICLR.
[31] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[32] Olivier Sigaud,et al. Tensor Based Knowledge Transfer Across Skill Categories for Robot Control , 2017, IJCAI.
[33] Bharath Hariharan,et al. Low-Shot Visual Recognition by Shrinking and Hallucinating Features , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[34] Li Zhang,et al. Learning to Learn: Meta-Critic Networks for Sample Efficient Learning , 2017, ArXiv.
[35] Yongxin Yang,et al. Deeper, Broader and Artier Domain Generalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[36] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[37] Andrea Vedaldi,et al. Learning multiple visual domains with residual adapters , 2017, NIPS.
[38] Tsuyoshi Murata,et al. {m , 1934, ACML.