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The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years.
H. F. Harlow, “The Formation Of Learning Sets.” Psychological Review , 1949
1949
Earlier work this paper cites.
H. Stackelberg, The Theory Of Market Economy . Oxford University Press, 1952
1952
Earlier work this paper cites.
A. M. Schrier, “Learning How To Learn: The Significance And Current Status Of Learning Set Formation,” Primates , 1984
1984
Earlier work this paper cites.
J. B. Biggs, “The Role of Meta-Learning in Study Processes,” British Journal of Educational Psychology , 1985
1985
Earlier work this paper cites.
J. Schmidhuber, “Evolutionary Principles In Self-referential Learning,” On learning how to learn: The meta-meta-… hook , 1987
1987
Earlier work this paper cites.
R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” Neural Computation , vol. 1, no. 2, pp. 270–280, 1989
1989
Earlier work this paper cites.
Y. Bengio, S. Bengio, and J. Cloutier, “Learning A Synaptic Learning Rule,” in IJCNN , 1990
1990
Earlier work this paper cites.
——, “A possibility for implementing curiosity and boredom in model-building neural controllers,” in SAB , 1991
1991
Earlier work this paper cites.
L. Y. Pratt, J. Mostow, C. A. Kamm, and A. A. Kamm, “Direct transfer of learned information among neural networks.” in AAAI , vol. 91, 1991
1991
Earlier work this paper cites.
R. J. Williams, “Simple Statistical Gradient-Following Algorithms For Connectionist Reinforcement Learning,” Machine learning , 1992
1992
Earlier work this paper cites.
J. Schmidhuber, “A Neural Network That Embeds Its Own Meta-levels,” in IEEE International Conference On Neural Networks , 1993
1993
Earlier work this paper cites.
J. L. Elman, “Learning and development in neural networks: the importance of starting small,” Cognition , vol. 48, no. 1, pp. 71 – 99, 1993
1993
Earlier work this paper cites.
M. B. Ring, “Continual learning in reinforcement environments,” Ph.D. dissertation, USA, 1994
1994
Earlier work this paper cites.
S. Bengio, Y. Bengio, and J. Cloutier, “On The Search For New Learning Rules For ANNs,” Neural Processing Letters , 1995
1995
Earlier work this paper cites.
J. Storck, S. Hochreiter, and J. Schmidhuber, “Reinforcement driven information acquisition in non-deterministic environments,” in ICANN , 1995
1995
Earlier work this paper cites.
D. H. Wolpert, “The Lack Of A Priori Distinctions Between Learning Algorithms,” Neural Computation , 1996
1996
Earlier work this paper cites.
J. Schmidhuber, J. Zhao, and M. Wiering, “Simple Principles Of Meta-Learning,” Technical report IDSIA , 1996
1996
Earlier work this paper cites.
J. Schmidhuber, J. Zhao, and M. Wiering, “Shifting Inductive Bias With Success-Story Algorithm, Adaptive Levin Search, And Incremental Self-Improvement,” Machine Learning , 1997
1997
Earlier work this paper cites.
R. Caruana, “Multitask Learning,” Machine Learning , 1997
1997
Earlier work this paper cites.
J. Schmidhuber, “What’s interesting?” 1997
1997
Earlier work this paper cites.
S. Thrun and L. Pratt, “Learning To Learn: Introduction And Overview,” in Learning To Learn , 1998
1998
Earlier work this paper cites.
S. Thrun, “Lifelong learning algorithms,” in Learning to learn . Springer, 1998, pp. 181–209
1998
Earlier work this paper cites.
J. Baxter, “Theoretical models of learning to learn,” in Learning to learn . Springer, 1998, pp. 71–94
1998
Earlier work this paper cites.
M. Wiering and J. Schmidhuber, “Efficient model-based exploration,” in SAB , 1998
1998
Earlier work this paper cites.
T. Heskes, “Empirical bayes for learning to learn,” in ICML , 2000
2000
Earlier work this paper cites.
S. Hochreiter, A. S. Younger, and P. R. Conwell, “Learning To Learn Using Gradient Descent,” in ICANN , 2001
2001
Earlier work this paper cites.
A. S. Younger, S. Hochreiter, and P. R. Conwell, “Meta-learning With Backpropagation,” in IJCNN , 2001
2001
Earlier work this paper cites.
R. Vilalta and Y. Drissi, “A Perspective View And Survey Of Meta-learning,” Artificial intelligence review , 2002
2002
Earlier work this paper cites.
N. Schweighofer and K. Doya, “Meta-learning In Reinforcement Learning,” Neural Networks , 2003
2003
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirchlet allocation,” Journal of Machine Learning Research , vol. 3, pp. 993–1022, 2003
2003
Earlier work this paper cites.
D. G. Lowe, “Distinctive Image Features From Scale-Invariant,” International Journal of Computer Vision , 2004
2004
Earlier work this paper cites.
J. Bayer, D. Wierstra, J. Togelius, and J. Schmidhuber, “Evolving memory cell structures for sequence learning,” in ICANN , 2009
2009
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum Learning,” in ICML , 2009
2009
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A Survey On Transfer Learning,” IEEE TKDE , 2010
2010
Earlier work this paper cites.
Z. Kang, K. Grauman, and F. Sha, “Learning With Whom To Share In Multi-task Feature Learning,” in ICML , 2011
2011
Earlier work this paper cites.
P. Domingos, “A Few Useful Things To Know About Machine Learning,” Commun. ACM , 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet Classification With Deep Convolutional Neural Networks,” in NeurIPS , 2012
2012
Earlier work this paper cites.
J. Bergstra and Y. Bengio, “Random Search For Hyper-Parameter Optimization,” in Journal Of Machine Learning Research , 2012
2012
Earlier work this paper cites.
B. Settles, “Active Learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning , 2012
2012
Earlier work this paper cites.
F. Stulp and O. Sigaud, “Robot Skill Learning: From Reinforcement Learning To Evolution Strategies,” Paladyn, Journal of Behavioral Robotics , 2013
2013
Earlier work this paper cites.
K. Muandet, D. Balduzzi, and B. Schölkopf, “Domain Generalization Via Invariant Feature Representation,” in ICML , 2013
2013
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How Transferable Are Features In Deep Neural Networks?” in NeurIPS , 2014
2014
Earlier work this paper cites.
A. Graves, G. Wayne, and I. Danihelka, “Neural Turing Machines,” in ArXiv E-prints , 2014
2014
Earlier work this paper cites.
C. Lemke, M. Budka, and B. Gabrys, “Meta-Learning: A Survey Of Trends And Technologies,” Artificial intelligence review , 2015
2015
Earlier work this paper cites.
G. Kosh, R. Zemel, and R. Salakhutdinov, “Siamese Neural Networks For One-shot Image Recognition,” in ICML , 2015
2015
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A Method For Stochastic Optimization,” in ICLR , 2015
2015
Earlier work this paper cites.
D. Maclaurin, D. Duvenaud, and R. P. Adams, “Gradient-based Hyperparameter Optimization Through Reversible Learning,” in ICML , 2015
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision , 2015
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining And Harnessing Adversarial Examples,” in ICLR , 2015
2015
Earlier work this paper cites.
Y. Yang and T. Hospedales, “A Unified Perspective On Multi-Domain And Multi-Task Learning,” in ICLR , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning For Image Recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering The Game Of Go With Deep Neural Networks And Tree Search,” Nature , 2016
2016
Earlier work this paper cites.
H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. S. Pande, “Low Data Drug Discovery With One-shot Learning,” CoRR , 2016
2016
Earlier work this paper cites.
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas, “Learning To Learn By Gradient Descent By Gradient Descent,” in NeurIPS , 2016
2016
Earlier work this paper cites.
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel, “RL 2 : Fast Reinforcement Learning Via Slow Reinforcement Learning,” in ArXiv E-prints , 2016
2016
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization As A Model For Few-Shot Learning,” in ICLR , 2016
2016
Earlier work this paper cites.
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas, “Taking The Human Out Of The Loop: A Review Of Bayesian Optimization,” Proceedings of the IEEE , 2016
2016
Earlier work this paper cites.
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap, “Meta Learning With Memory-Augmented Neural Networks,” in ICML , 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching Networks For One Shot Learning,” in NeurIPS , 2016
2016
Earlier work this paper cites.
J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick, “Learning To Reinforcement Learn,” CoRR , 2016
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Online Batch Selection For Faster Training Of Neural Networks,” in ICLR , 2016
2016
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “OpenAI Gym,” 2016
2016
Earlier work this paper cites.
F. Pedregosa, “Hyperparameter optimization with approximate gradient,” in ICML , 2016
2016
Earlier work this paper cites.
J. Fu, H. Luo, J. Feng, K. H. Low, and T.-S. Chua, “DrMAD: Distilling reverse-mode automatic differentiation for optimizing hyperparameters of deep neural networks,” in IJCAI , 2016
2016
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-Agnostic Meta-learning For Fast Adaptation Of Deep Networks,” in ICML , 2017
2017
Earlier work this paper cites.
J. Snell, K. Swersky, and R. S. Zemel, “Prototypical Networks For Few Shot Learning,” in NeurIPS , 2017
2017
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural Architecture Search With Reinforcement Learning,” ICLR , 2017
2017
Earlier work this paper cites.
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola, “Deep sets,” in NIPS , 2017
2017
Earlier work this paper cites.
G. Csurka, Domain Adaptation In Computer Vision Applications . Springer, 2017
2017
Earlier work this paper cites.
Y. Yang and T. M. Hospedales, “Deep Multi-Task Representation Learning: A Tensor Factorisation Approach,” in ICLR , 2017
2017
Earlier work this paper cites.
L. Franceschi, M. Donini, P. Frasconi, and M. Pontil, “Forward And Reverse Gradient-Based Hyperparameter Optimization,” in ICML , 2017
2017
Earlier work this paper cites.
H. Edwards and A. Storkey, “Towards A Neural Statistician,” in ICLR , 2017
2017
Earlier work this paper cites.
Z. Li, F. Zhou, F. Chen, and H. Li, “Meta-SGD: Learning To Learn Quickly For Few Shot Learning,” arXiv e-prints , 2017
2017
Earlier work this paper cites.
K. Li and J. Malik, “Learning To Optimize,” in ICLR , 2017
2017
Earlier work this paper cites.
T. Munkhdalai and H. Yu, “Meta Networks,” in ICML , 2017
2017
Earlier work this paper cites.
I. Bello, B. Zoph, V. Vasudevan, and Q. V. Le, “Neural Optimizer Search With Reinforcement Learning,” in ICML , 2017
2017
Earlier work this paper cites.
O. Wichrowska, N. Maheswaranathan, M. W. Hoffman, S. G. Colmenarejo, M. Denil, N. de Freitas, and J. Sohl-Dickstein, “Learned Optimizers That Scale And Generalize,” in ICML , 2017
2017
Earlier work this paper cites.
Y. Chen, M. W. Hoffman, S. G. Colmenarejo, M. Denil, T. P. Lillicrap, M. Botvinick, and N. de Freitas, “Learning To Learn Without Gradient Descent By Gradient Descent,” in ICML , 2017
2017
Earlier work this paper cites.
D. Ha, A. Dai, and Q. V. Le, “HyperNetworks,” ICLR , 2017
2017
Earlier work this paper cites.
Y. Duan, M. Andrychowicz, B. Stadie, O. J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba, “One-shot Imitation Learning,” in NeurIPS , 2017
2017
Earlier work this paper cites.
F. Sung, L. Zhang, T. Xiang, T. Hospedales, and Y. Yang, “Learning To Learn: Meta-critic Networks For Sample Efficient Learning,” arXiv e-prints , 2017
2017
Earlier work this paper cites.
C. Doersch and A. Zisserman, “Multi-task Self-Supervised Visual Learning,” in ICCV , 2017
2017
Earlier work this paper cites.
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu, “Reinforcement Learning With Unsupervised Auxiliary Tasks,” in ICLR , 2017
2017
Earlier work this paper cites.
C. Fernando, D. Banarse, C. Blundell, Y. Zwols, D. Ha, A. A. Rusu, A. Pritzel, and D. Wierstra, “PathNet: Evolution Channels Gradient Descent In Super Neural Networks,” in ArXiv E-prints , 2017
2017
Earlier work this paper cites.
A. Antoniou, A. Storkey, and H. Edwards, “Data Augmentation Generative Adversarial Networks,” arXiv e-prints , 2017
2017
Earlier work this paper cites.
Q. V. L. Prajit Ramachandran, Barret Zoph, “Searching For Activation Functions,” in ArXiv E-prints , 2017
2017
Earlier work this paper cites.
P. Bachman, A. Sordoni, and A. Trischler, “Learning Algorithms For Active Learning,” in ICML , 2017
2017
Earlier work this paper cites.
K. Konyushkova, R. Sznitman, and P. Fua, “Learning Active Learning From Data,” in NeurIPS , 2017
2017
Earlier work this paper cites.
T. Salimans, J. Ho, X. Chen, S. Sidor, and I. Sutskever, “Evolution Strategies As A Scalable Alternative To Reinforcement Learning,” arXiv e-prints , 2017
2017
Earlier work this paper cites.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting Unreasonable Effectiveness Of Data In Deep Learning Era,” in ICCV , 2017
2017
Earlier work this paper cites.
A. Shaban, S. Bansal, Z. Liu, I. Essa, and B. Boots, “One-Shot Learning For Semantic Segmentation,” CoRR , 2017
2017
Earlier work this paper cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning Multiple Visual Domains With Residual Adapters,” in NeurIPS , 2017
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” arXiv e-prints , 2017
2017
Earlier work this paper cites.
D. Li, Y. Yang, Y.-Z. Song, and T. Hospedales, “Deeper, Broader And Artier Domain Generalization,” in ICCV , 2017
2017
Earlier work this paper cites.
J. Devlin, R. Bunel, R. Singh, M. J. Hausknecht, and P. Kohli, “Neural Program Meta-Induction,” in NIPS , 2017
2017
Earlier work this paper cites.
T. O’Shea and J. Hoydis, “An Introduction To Deep Learning For The Physical Layer,” IEEE Transactions on Cognitive Communications and Networking , 2017
2017
Earlier work this paper cites.
M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle, “A meta-learning perspective on cold-start recommendations for items,” in NIPS , 2017
2017
Earlier work this paper cites.
G. Marcus, “Deep Learning: A Critical Appraisal,” arXiv e-prints , 2018
2018
Earlier work this paper cites.
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil, “Bilevel Programming For Hyperparameter Optimization And Meta-learning,” in ICML , 2018
2018
Earlier work this paper cites.
R. Houthooft, R. Y. Chen, P. Isola, B. C. Stadie, F. Wolski, J. Ho, and P. Abbeel, “Evolved Policy Gradients,” NeurIPS , 2018
2018
Earlier work this paper cites.
J. Vanschoren, “Meta-Learning: A Survey,” CoRR , 2018
2018
Cited alongside, same era.
Q. Yao, M. Wang, H. J. Escalante, I. Guyon, Y. Hu, Y. Li, W. Tu, Q. Yang, and Y. Yu, “Taking Human Out Of Learning Applications: A Survey On Automated Machine Learning,” CoRR , 2018
2018
Cited alongside, same era.
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel, “A Simple Neural Attentive Meta-learner,” ICLR , 2018
2018
Cited alongside, same era.
A. Sinha, P. Malo, and K. Deb, “A Review On Bilevel Optimization: From Classical To Evolutionary Approaches And Applications,” IEEE Transactions on Evolutionary Computation , 2018
2018
Cited alongside, same era.
G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil, “Learning To Learn Around A Common Mean,” in NeurIPS , 2018
2018
Cited alongside, same era.
R. Hou, H. Chang, M. Bingpeng, S. Shan, and X. Chen, “Cross Attention Network For Few-shot Classification,” in NeurIPS , 2019
2019
Later among the works it cites.
M. Ren, R. Liao, E. Fetaya, and R. Zemel, “Incremental Few-shot Learning With Attention Attractor Networks,” in NeurIPS , 2019
2019
Later among the works it cites.
F. Alet, E. Weng, T. Lozano-Pérez, and L. P. Kaelbling, “Neural Relational Inference With Fast Modular Meta-learning,” in NeurIPS , 2019
2019
Later among the works it cites.
B. M. Lake, “Compositional Generalization Through Meta Sequence-to-sequence Learning,” in NeurIPS , 2019
2019
Later among the works it cites.
E. D. Cubuk, B. Zoph, D. Mané, V. Vasudevan, and Q. V. Le, “AutoAugment: Learning Augmentation Policies From Data,” CVPR , 2019
2019
Later among the works it cites.
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Z. Chen and B. Liu, “Lifelong Machine Learning, Second Edition,” Synthesis Lectures on Artificial Intelligence and Machine Learning , 2018
2018
Cited alongside, same era.
M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel, “Continuous Adaptation Via Meta-Learning In Nonstationary And Competitive Environments,” ICLR , 2018
2018
Cited alongside, same era.
S. Ritter, J. X. Wang, Z. Kurth-Nelson, S. M. Jayakumar, C. Blundell, R. Pascanu, and M. Botvinick, “Been There, Done That: Meta-learning With Episodic Recall,” ICML , 2018
2018
Cited alongside, same era.
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths, “Recasting Gradient-Based Meta-Learning As Hierarchical Bayes,” in ICLR , 2018
2018
Cited alongside, same era.
S. C. Yoonho Lee, “Gradient-Based Meta-Learning With Learned Layerwise Metric And Subspace,” in ICML , 2018
2018
Cited alongside, same era.
A. Antoniou, H. Edwards, and A. J. Storkey, “How To Train Your MAML,” in ICLR , 2018
2018
Cited alongside, same era.
S. Qiao, C. Liu, W. Shen, and A. L. Yuille, “Few-Shot Image Recognition By Predicting Parameters From Activations,” CVPR , 2018
2018
Cited alongside, same era.
R. Volpi and V. Murino, “Model Vulnerability To Distributional Shifts Over Image Transformation Sets,” in ICCV , 2019
2019
Later among the works it cites.
C. Zhang, C. Öztireli, S. Mandt, and G. Salvi, “Active Mini-batch Sampling Using Repulsive Point Processes,” in AAAI , 2019
2019
Later among the works it cites.
J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng, “Meta-Weight-Net: Learning An Explicit Mapping For Sample Weighting,” in NeurIPS , 2019
2019
Later among the works it cites.
W.-H. Li, C.-S. Foo, and H. Bilen, “Learning To Impute: A General Framework For Semi-supervised Learning,” arXiv e-prints , 2019
2019
Later among the works it cites.
Q. Sun, X. Li, Y. Liu, S. Zheng, T.-S. Chua, and B. Schiele, “Learning To Self-train For Semi-supervised Few-shot Classification,” in NeurIPS , 2019
2019
Later among the works it cites.
Q. Vuong, S. Vikram, H. Su, S. Gao, and H. I. Christensen, “How To Pick The Domain Randomization Parameters For Sim-to-real Transfer Of Reinforcement Learning Policies?” CoRR , 2019
2019
Later among the works it cites.
K. Lee, S. Maji, A. Ravichandran, and S. Soatto, “Meta-Learning With Differentiable Convex Optimization,” in CVPR , 2019
2019
Later among the works it cites.
A. Rajeswaran, C. Finn, S. Kakade, and S. Levine, “Meta-Learning With Implicit Gradients,” in NeurIPS , 2019
2019
Later among the works it cites.
L. Bertinetto, J. F. Henriques, P. H. Torr, and A. Vedaldi, “Meta-learning With Differentiable Closed-form Solvers,” in ICLR , 2019
2019
Later among the works it cites.
H. Liu, R. Socher, and C. Xiong, “Taming MAML: Efficient Unbiased Meta-reinforcement Learning,” in ICML , 2019
2019
Later among the works it cites.
J. Rothfuss, D. Lee, I. Clavera, T. Asfour, and P. Abbeel, “ProMP: Proximal Meta-Policy Search,” in ICLR , 2019
2019
Later among the works it cites.
K. Young, B. Wang, and M. E. Taylor, “Metatrace Actor-Critic: Online Step-Size Tuning By Meta-gradient Descent For Reinforcement Learning Control,” in IJCAI , 2019
2019
Later among the works it cites.
M. Jaderberg, W. M. Czarnecki, I. Dunning, L. Marris, G. Lever, A. G. Castañeda, C. Beattie, N. C. Rabinowitz, A. S. Morcos, A. Ruderman, N. Sonnerat, T. Green, L. Deason, J. Z. Leibo, D. Silver, D. Hassabis, K. Kavukcuoglu, and T. Graepel, “Human-level Performance In 3D Multiplayer Games With Population-based Reinforcement Learning,” Science , 2019
2019
Later among the works it cites.
A. Pakman, Y. Wang, C. Mitelut, J. Lee, and L. Paninski, “Neural clustering processes,” in ICML , 2019
2019
Later among the works it cites.
J. Lee, Y. Lee, J. Kim, A. Kosiorek, S. Choi, and Y. W. Teh, “Set transformer: A framework for attention-based permutation-invariant neural networks,” in ICML , 2019
2019
Later among the works it cites.
J. Lee, Y. Lee, and Y. W. Teh, “Deep amortized clustering,” 2019
2019
Later among the works it cites.
V. Veeriah, M. Hessel, Z. Xu, R. Lewis, J. Rajendran, J. Oh, H. van Hasselt, D. Silver, and S. Singh, “Discovery Of Useful Questions As Auxiliary Tasks,” in NeurIPS , 2019
2019
Later among the works it cites.
F. Garcia and P. S. Thomas, “A Meta-MDP Approach To Exploration For Lifelong Reinforcement Learning,” in NeurIPS , 2019
2019
Later among the works it cites.
C. Russell, M. Toso, and N. Campbell, “Fixing Implicit Derivatives: Trust-Region Based Learning Of Continuous Energy Functions,” in NeurIPS , 2019
2019
Later among the works it cites.
Y. Cao, T. Chen, Z. Wang, and Y. Shen, “Learning To Optimize In Swarms,” in NeurIPS , 2019
2019
Later among the works it cites.
S. Chen, W. Wang, and S. J. Pan, “MetaQuant: Learning To Quantize By Learning To Penetrate Non-differentiable Quantization,” in NeurIPS , 2019
2019
Later among the works it cites.
S. W. Yoon, J. Seo, and J. Moon, “Tapnet: Neural Network Augmented With Task-adaptive Projection For Few-shot Learning,” ICML , 2019
2019
Later among the works it cites.
J. W. Rae, S. Bartunov, and T. P. Lillicrap, “Meta-learning Neural Bloom Filters,” ICML , 2019
2019
Later among the works it cites.
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals, “Rapid Learning Or Feature Reuse? Towards Understanding The Effectiveness Of Maml,” arXiv e-prints , 2019
2019
Later among the works it cites.
B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng, and T. Darrell, “Few-shot Object Detection Via Feature Reweighting,” in ICCV , 2019
2019
Later among the works it cites.
K. Rakelly, E. Shelhamer, T. Darrell, A. A. Efros, and S. Levine, “Few-Shot Segmentation Propagation With Guided Networks,” ICML , 2019
2019
Later among the works it cites.
E. Zakharov, A. Shysheya, E. Burkov, and V. S. Lempitsky, “Few-Shot Adversarial Learning Of Realistic Neural Talking Head Models,” CoRR , 2019
2019
Later among the works it cites.
T.-C. Wang, M.-Y. Liu, A. Tao, G. Liu, J. Kautz, and B. Catanzaro, “Few-shot Video-to-video Synthesis,” in NeurIPS , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
T. de Vries, I. Misra, C. Wang, and L. van der Maaten, “Does Object Recognition Work For Everyone?” in CVPR , 2019
2019
Later among the works it cites.
O. Sigaud and F. Stulp, “Policy Search In Continuous Action Domains: An Overview,” Neural Networks , 2019
2019
Later among the works it cites.
O. Kroemer, S. Niekum, and G. D. Konidaris, “A Review Of Robot Learning For Manipulation: Challenges, Representations, And Algorithms,” CoRR , 2019
2019
Later among the works it cites.
A. Jabri, K. Hsu, A. Gupta, B. Eysenbach, S. Levine, and C. Finn, “Unsupervised Curricula For Visual Meta-Reinforcement Learning,” in NeurIPS , 2019
2019
Later among the works it cites.
Y. Yang, K. Caluwaerts, A. Iscen, J. Tan, and C. Finn, “Norml: No-reward Meta Learning,” in AAMAS , 2019
2019
Later among the works it cites.
S. K. Seyed Ghasemipour, S. S. Gu, and R. Zemel, “SMILe: Scalable Meta Inverse Reinforcement Learning Through Context-Conditional Policies,” in NeurIPS , 2019
2019
Later among the works it cites.
K. Cobbe, O. Klimov, C. Hesse, T. Kim, and J. Schulman, “Quantifying Generalization In Reinforcement Learning,” ICML , 2019
2019
Later among the works it cites.
C. Zhao, O. Siguad, F. Stulp, and T. M. Hospedales, “Investigating Generalisation In Continuous Deep Reinforcement Learning,” arXiv e-prints , 2019
2019
Later among the works it cites.
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A Benchmark And Evaluation For Multi-task And Meta Reinforcement Learning,” CORL , 2019
2019
Later among the works it cites.
A. Bakhtin, L. van der Maaten, J. Johnson, L. Gustafson, and R. Girshick, “Phyre: A New Benchmark For Physical Reasoning,” in NeurIPS , 2019
2019
Later among the works it cites.
A. Kar, A. Prakash, M. Liu, E. Cameracci, J. Yuan, M. Rusiniak, D. Acuna, A. Torralba, and S. Fidler, “Meta-Sim: Learning To Generate Synthetic Datasets,” CoRR , 2019
2019
Later among the works it cites.
T. Elsken, B. Staffler, J. H. Metzen, and F. Hutter, “Meta-Learning Of Neural Architectures For Few-Shot Learning,” in CVPR , 2019
2019
Later among the works it cites.
L. Li and A. Talwalkar, “Random Search And Reproducibility For Neural Architecture Search,” arXiv e-prints , 2019
2019
Later among the works it cites.
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter, “NAS-Bench-101: Towards Reproducible Neural Architecture Search,” in ICML , 2019
2019
Later among the works it cites.
P. Tossou, B. Dura, F. Laviolette, M. Marchand, and A. Lacoste, “Adaptive Deep Kernel Learning,” CoRR , 2019
2019
Later among the works it cites.
M. Patacchiola, J. Turner, E. J. Crowley, M. O’Boyle, and A. Storkey, “Deep Kernel Transfer In Gaussian Processes For Few-shot Learning,” arXiv e-prints , 2019
2019
Later among the works it cites.
S. Ravi and A. Beatson, “Amortized Bayesian Meta-Learning,” in ICLR , 2019
2019
Later among the works it cites.
K. Hsu, S. Levine, and C. Finn, “Unsupervised Learning Via Meta-learning,” ICLR , 2019
2019
Later among the works it cites.
S. Khodadadeh, L. Boloni, and M. Shah, “Unsupervised Meta-Learning For Few-Shot Image Classification,” in NeurIPS , 2019
2019
Later among the works it cites.
A. Antoniou and A. Storkey, “Assume, Augment And Learn: Unsupervised Few-shot Meta-learning Via Random Labels And Data Augmentation,” arXiv e-prints , 2019
2019
Later among the works it cites.
Y. Jiang and N. Verma, “Meta-Learning To Cluster,” 2019
2019
Later among the works it cites.
K. Javed and M. White, “Meta-learning Representations For Continual Learning,” in NeurIPS , 2019
2019
Later among the works it cites.
T. Miconi, A. Rawal, J. Clune, and K. O. Stanley, “Backpropamine: Training Self-modifying Neural Networks With Differentiable Neuromodulated Plasticity,” in ICLR , 2019
2019
Later among the works it cites.
Y. Xie, H. Jiang, F. Liu, T. Zhao, and H. Zha, “Meta Learning With Relational Information For Short Sequences,” in NeurIPS , 2019
2019
Later among the works it cites.
Z. Lin, A. Madotto, C. Wu, and P. Fung, “Personalizing Dialogue Agents Via Meta-Learning,” CoRR , 2019
2019
Later among the works it cites.
J.-Y. Hsu, Y.-J. Chen, and H. yi Lee, “Meta Learning For End-to-End Low-Resource Speech Recognition,” in ICASSP , 2019
2019
Later among the works it cites.
D. M. Metter, T. J. Colgan, S. T. Leung, C. F. Timmons, and J. Y. Park, “Trends In The US And Canadian Pathologist Workforces From 2007 To 2017,” JAMA Network Open , 2019
2019
Later among the works it cites.
B. D. Nguyen, T.-T. Do, B. X. Nguyen, T. Do, E. Tjiputra, and Q. D. Tran, “Overcoming Data Limitation In Medical Visual Question Answering,” arXiv e-prints , 2019
2019
Later among the works it cites.
Z. Mirikharaji, Y. Yan, and G. Hamarneh, “Learning To Segment Skin Lesions From Noisy Annotations,” CoRR , 2019
2019
Later among the works it cites.
K. Zheng, Z.-J. Zha, and W. Wei, “Abstract Reasoning With Distracting Features,” in NeurIPS , 2019
2019
Later among the works it cites.
Z. Liu, H. Mu, X. Zhang, Z. Guo, X. Yang, K.-T. Cheng, and J. Sun, “Metapruning: Meta Learning For Automatic Neural Network Channel Pruning,” in ICCV , 2019
2019
Later among the works it cites.
Y. Jiang, H. Kim, H. Asnani, and S. Kannan, “MIND: Model Independent Neural Decoder,” arXiv e-prints , 2019
2019
Later among the works it cites.
H. Bharadhwaj, “Meta-learning for user cold-start recommendation,” in IJCNN , 2019
2019
Later among the works it cites.
K. Allen, E. Shelhamer, H. Shin, and J. Tenenbaum, “Infinite Mixture Prototypes For Few-shot Learning,” in ICML , 2019
2019
Later among the works it cites.
A. Shaban, C.-A. Cheng, N. Hatch, and B. Boots, “Truncated back-propagation for bilevel optimization,” in AISTATS , 2019
2019
Later among the works it cites.
S. Flennerhag, P. G. Moreno, N. Lawrence, and A. Damianou, “Transferring knowledge across learning processes,” in ICLR , 2019
2019
Later among the works it cites.
F. Alet, M. F. Schneider, T. Lozano-Perez, and L. Pack Kaelbling, “Meta-Learning Curiosity Algorithms,” ICLR , 2020
2020
Closest in time.
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” ACM Comput. Surv. , vol. 53, no. 3, Jun. 2020
2020
Closest in time.
D. Li and T. Hospedales, “Online Meta-Learning For Multi-Source And Semi-Supervised Domain Adaptation,” in ECCV , 2020
2020
Closest in time.
P. Micaelli and A. Storkey, “Non-greedy gradient-based hyperparameter optimization over long horizons,” arXiv , 2020
2020
Closest in time.
H. Yao, X. Wu, Z. Tao, Y. Li, B. Ding, R. Li, and Z. Li, “Automated Relational Meta-learning,” in ICLR , 2020
2020
Closest in time.
S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell, “Meta-learning with warped gradient descent,” in ICLR , 2020
2020
Closest in time.
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P. Manzagol, and H. Larochelle, “Meta-Dataset: A Dataset Of Datasets For Learning To Learn From Few Examples,” ICLR , 2020
2020
Closest in time.
H.-Y. Tseng, H.-Y. Lee, J.-B. Huang, and M.-H. Yang, “”Cross-Domain Few-Shot Classification Via Learned Feature-Wise Transformation”,” ICLR , Jan. 2020
2020
Closest in time.
W. Zhou, Y. Li, Y. Yang, H. Wang, and T. M. Hospedales, “Online Meta-Critic Learning For Off-Policy Actor-Critic Methods,” in NeurIPS , 2020
2020
Closest in time.
A. Zela, T. Elsken, T. Saikia, Y. Marrakchi, T. Brox, and F. Hutter, “Understanding and robustifying differentiable architecture search,” in ICLR , 2020. [Online]. Available: https://openreview.net/forum?id=H1gDNyrKDS
2020
Closest in time.
D. Lian, Y. Zheng, Y. Xu, Y. Lu, L. Lin, P. Zhao, J. Huang, and S. Gao, “Towards Fast Adaptation Of Neural Architectures With Meta Learning,” in ICLR , 2020
2020
Closest in time.
Y. Bao, M. Wu, S. Chang, and R. Barzilay, “Few-shot Text Classification With Distributional Signatures,” in ICLR , 2020
2020
Closest in time.
Y. Li, G. Hu, Y. Wang, T. Hospedales, N. M. Robertson, and Y. Yang, “DADA: Differentiable Automatic Data Augmentation,” 2020
2020
Closest in time.
J. Lorraine, P. Vicol, and D. Duvenaud, “Optimizing Millions Of Hyperparameters By Implicit Differentiation,” in AISTATS , 2020
2020
Closest in time.
O. Bohdal, Y. Yang, and T. Hospedales, “Flexible dataset distillation: Learn labels instead of images,” arXiv , 2020
2020
Closest in time.
O. M. Andrychowicz, B. Baker, M. Chociej, R. Józefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba, “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
Closest in time.
H. B. Lee, H. Lee, D. Na, S. Kim, M. Park, E. Yang, and S. J. Hwang, “Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks,” ICLR , 2020
2020
Closest in time.
R. Fakoor, P. Chaudhari, S. Soatto, and A. J. Smola, “Meta-Q-Learning,” in ICLR , 2020
2020
Closest in time.
X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang, “ES-MAML: Simple Hessian-Free Meta Learning,” in ICLR , 2020
2020
Closest in time.
J.-M. Perez-Rua, X. Zhu, T. Hospedales, and T. Xiang, “Incremental Few-Shot Object Detection,” in CVPR , 2020
2020
Closest in time.
H. B. Lee, T. Nam, E. Yang, and S. J. Hwang, “Meta Dropout: Learning To Perturb Latent Features For Generalization,” in ICLR , 2020
2020
Closest in time.
M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn, “Meta-Learning Without Memorization,” ICLR , 2020
2020
Closest in time.
A. Antoniou and M. O. S. A. Massimiliano, Patacchiola, “Defining Benchmarks For Continual Few-shot Learning,” arXiv e-prints , 2020
2020
Closest in time.
A. Fallah, A. Mokhtari, and A. Ozdaglar, “Provably Convergent Policy Gradient Methods For Model-Agnostic Meta-Reinforcement Learning,” arXiv e-prints , 2020
2020
Closest in time.
L. Kirsch, S. van Steenkiste, and J. Schmidhuber, “Improving Generalization In Meta Reinforcement Learning Using Learned Objectives,” in ICLR , 2020
2020
Closest in time.
Z. Wang, Y. Zhao, P. Yu, R. Zhang, and C. Chen, “Bayesian meta sampling for fast uncertainty adaptation,” in ICLR , 2020
2020
Closest in time.
A. Sinitsin, V. Plokhotnyuk, D. Pyrkin, S. Popov, and A. Babenko, “Editable Neural Networks,” in ICLR , 2020
2020
Closest in time.
G. I. Winata, S. Cahyawijaya, Z. Liu, Z. Lin, A. Madotto, P. Xu, and P. Fung, “Learning Fast Adaptation On Cross-Accented Speech Recognition,” arXiv e-prints , 2020
2020
Closest in time.
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn, “Gradient Surgery For Multi-Task Learning,” 2020
2020
Closest in time.