Meta Learning Papers

Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
Alternatives To Meta Learning Papers
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
5 days ago3mitPython
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Auto Sklearn6,80115153 days ago42September 20, 2022153bsd-3-clausePython
Automated Machine Learning with scikit-learn
Meta Learning Papers2,374
4 years ago4
Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning
Learn2learn2,03015 months ago18February 10, 202239mitPython
A PyTorch Library for Meta-learning Research
Pytorch Meta1,724
4 months ago28September 20, 202153mitPython
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
Awesome Meta Learning917
2 years ago1
A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
3 years ago20mitPython
PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)
Hands On Meta Learning With Python857
2 years ago4Jupyter Notebook
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
Awesome Papers Fewshot752
10 months agomitTeX
Collection for Few-shot Learning
Pytorch Maml Rl645
a year ago20mitPython
Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch
Alternatives To Meta Learning Papers
Select To Compare

Alternative Project Comparisons

Meta Learning/ Learning to Learn/ One Shot Learning/ Lifelong Learning

1 Legacy Papers

[1] Nicolas Schweighofer and Kenji Doya. Meta-learning in reinforcement learning. Neural Networks, 16(1):59, 2003.

[2] Sepp Hochreiter, A Steven Younger, and Peter R Conwell. Learning to learn using gradient descent. In International Conference on Artificial Neural Networks, pages 8794. Springer, 2001.

[3] Kunikazu Kobayashi, Hiroyuki Mizoue, Takashi Kuremoto, and Masanao Obayashi. A meta-learning method based on temporal difference error. In International Conference on Neural Information Processing, pages 530537. Springer, 2009.

[4] Sebastian Thrun and Lorien Pratt. Learning to learn: Introduction and overview. In Learning to learn, pages 317. Springer, 1998.

[5] A Steven Younger, Sepp Hochreiter, and Peter R Conwell. Meta-learning with backpropagation. In Neural Networks, 2001. Proceedings. IJCNN01. International Joint Conference on, volume 3. IEEE, 2001.

[6] Ricardo Vilalta and Youssef Drissi. A perspective view and survey of meta-learning. Artificial Intelligence Review, 18(2):7795, 2002.

[7] Hugo Larochelle, Dumitru Erhan, and Yoshua Bengio. Zero-data learning of new tasks. In AAAI, volume 1, pp. 3, 2008.

[8] Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum.One shot learning of simple visual concepts. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society, volume 172, pp. 2, 2011.

[9] Li Fei-Fei, Rob Fergus, and Pietro Perona. One-shot learning of object categories. IEEE transactions on pattern analysis and machine intelligence, 28(4):594611, 2006.

[10] Ju rgen Schmidhuber. A neural network that embeds its own meta-levels. In Neural Networks, 1993., IEEE International Conference on, pp. 407412. IEEE, 1993.

[11] Sebastian Thrun. Lifelong learning algorithms. In Learning to learn, pp. 181209. Springer, 1998.

[12] Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier. Learning a synaptic learning rule. Universite de Montre al, De partement dinformatique et de recherche ope rationnelle, 1990.

[13] Samy Bengio, Yoshua Bengio, and Jocelyn Cloutier. On the search for new learning rules for ANNs. Neural Processing Letters, 2(4):2630, 1995.

[14] Rich Caruana. Learning many related tasks at the same time with backpropagation. Advances in neural information processing systems, pp. 657664, 1995.

[15] Giraud-Carrier, Christophe, Vilalta, Ricardo, and Brazdil, Pavel. Introduction to the special issue on meta-learning. Machine learning, 54(3):187193, 2004.

[16] Jankowski, Norbert, Duch, Wodzisaw, and Grabczewski, Krzysztof. Meta-learning in computational intelligence, volume 358. Springer Science & Business Media, 2011.

[17] N. E. Cotter and P. R. Conwell. Fixed-weight networks can learn. In International Joint Conference on Neural Networks, pages 553559, 1990.

[18] J. Schmidhuber. Evolutionary principles in self-referential learning; On learning how to learn: The meta-meta-... hook. PhD thesis, Institut f. Informatik, Tech. Univ. Munich, 1987.

[19] J. Schmidhuber. Learning to control fast-weight memories: An alternative to dynamic recurrent networks. Neural Computation, 4(1):131139, 1992.

[20] Jurgen Schmidhuber, Jieyu Zhao, and Marco Wiering. Simple principles of metalearning. Technical report, SEE, 1996.

[21] Thrun, Sebastian and Pratt, Lorien. Learning to learn. Springer Science & Business Media, 1998.

2 Recent Papers

[1] Andrychowicz, Marcin, Denil, Misha, Gomez, Sergio, Hoffman, Matthew W, Pfau, David, Schaul, Tom, and de Freitas, Nando. Learning to learn by gradient descent by gradient descent. In Advances in Neural Information Processing Systems, pp. 39813989, 2016

[2] Ba, Jimmy, Hinton, Geoffrey E, Mnih, Volodymyr, Leibo, Joel Z, and Ionescu, Catalin. Using fast weights to attend to the recent past. In Advances In Neural Information Processing Systems, pp. 43314339, 2016

[3] David Ha, Andrew Dai and Le, Quoc V. Hypernetworks. In ICLR 2017, 2017.

[4] Koch, Gregory. Siamese neural networks for one-shot image recognition. PhD thesis, University of Toronto, 2015.

[5] Lake, Brenden M, Salakhutdinov, Ruslan R, and Tenenbaum, Josh. One-shot learning by inverting a compositional causal process. In Advances in neural information processing systems, pp. 25262534, 2013.

[6] Santoro, Adam, Bartunov, Sergey, Botvinick, Matthew, Wierstra, Daan, and Lillicrap, Timothy. Meta-learning with memory-augmented neural networks. In Proceedings of The 33rd International Conference on Machine Learning, pp. 18421850, 2016.

[7] Vinyals, Oriol, Blundell, Charles, Lillicrap, Tim, Wierstra, Daan, et al. Matching networks for one shot learning. In Advances in Neural Information Processing Systems, pp. 36303638, 2016.

[8] Kaiser, Lukasz, Nachum, Ofir, Roy, Aurko, and Bengio, Samy. Learning to remember rare events. In ICLR 2017, 2017.

[9] P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, D. Kumaran, and R. Hadsell. Learning to navigate in complex environments. Techni- cal report, DeepMind, 2016.

[10] B. Zoph and Q. V. Le. Neural architecture search with reinforcement learning. Technical report, submitted to ICLR 2017, 2016.

[11] Y. Duan, J. Schulman, X. Chen, P. Bartlett, I. Sutskever, and P. Abbeel. Rl2: Fast reinforcement learning via slow reinforcement learning. Technical report, UC Berkeley and OpenAI, 2016.

[12] Li, Ke and Malik, Jitendra. Learning to optimize. International Conference on Learning Representations (ICLR), 2017.

[13] Edwards, Harrison and Storkey, Amos. Towards a neural statistician. International Conference on Learning Representations (ICLR), 2017.

[14] Parisotto, Emilio, Ba, Jimmy Lei, and Salakhutdinov, Ruslan. Actor-mimic: Deep multitask and transfer reinforcement learning. International Conference on Learning Representations (ICLR), 2016.

[15] Ravi, Sachin and Larochelle, Hugo. Optimization as a model for few-shot learning. In International Conference on Learning Representations (ICLR), 2017.

[16] Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. arXiv preprint arXiv:1703.03400.

[17] Chen, Y., Hoffman, M. W., Colmenarejo, S. G., Denil, M., Lillicrap, T. P., & de Freitas, N. (2016). Learning to Learn for Global Optimization of Black Box Functions. arXiv preprint arXiv:1611.03824.

[18] Munkhdalai T, Yu H. Meta Networks. arXiv preprint arXiv:1703.00837, 2017.

[19] Duan Y, Andrychowicz M, Stadie B, et al. One-Shot Imitation Learning. arXiv preprint arXiv:1703.07326, 2017.

[20] Woodward M, Finn C. Active One-shot Learning. arXiv preprint arXiv:1702.06559, 2017.

[21] Wichrowska O, Maheswaranathan N, Hoffman M W, et al. Learned Optimizers that Scale and Generalize. arXiv preprint arXiv:1703.04813, 2017.

[22] Hariharan, Bharath, and Ross Girshick. Low-shot visual object recognition arXiv preprint arXiv:1606.02819 (2016).

[23] Wang J X, Kurth-Nelson Z, Tirumala D, et al. Learning to reinforcement learn. arXiv preprint arXiv:1611.05763, 2016.

[24] Flood Sung, Zhang L, Xiang T, Hospedales T, et al. Learning to Learn: Meta-Critic Networks for Sample Efficient Learning. arXiv preprint arXiv:1706.09529, 2017.

[25] Li Z, Zhou F, Chen F, et al. Meta-SGD: Learning to Learn Quickly for Few Shot Learning. arXiv preprint arXiv:1707.09835, 2017.

[26] Mishra N, Rohaninejad M, Chen X, et al. Meta-Learning with Temporal Convolutions. arXiv preprint arXiv:1707.03141, 2017.

[27] Frans K, Ho J, Chen X, et al. Meta Learning Shared Hierarchies. arXiv preprint arXiv:1710.09767, 2017.

[28] Finn C, Yu T, Zhang T, et al. One-shot visual imitation learning via meta-learning. arXiv preprint arXiv:1709.04905, 2017.

[29] Flood Sung, Yongxin Yang, Zhang Li, Xiang T,Philip Torr, Hospedales T, et al Learning to Compare: Relation Network for Few Shot Learning. arXiv preprint arXiv:1711.06025, 2017.

[30] Brenden M Lake, Ruslan Salakhutdinov, Joshua B Tenenbaum Human-level concept learning through probabilistic program induction. In Science, volume 350, pp. 1332-1338, 2015.

[32] Xu D, Nair S, Zhu Y, et al. Neural task programming: Learning to generalize across hierarchical tasks. arXiv preprint arXiv:1710.01813, 2017.

[33] Bertinetto, L., Henriques, J. F., Valmadre, J., Torr, P., & Vedaldi, A. (2016). Learning feed-forward one-shot learners. In Advances in Neural Information Processing Systems (pp. 523-531).

[34] Wang, Yu-Xiong, and Martial Hebert. Learning to learn: Model regression networks for easy small sample learning. European Conference on Computer Vision. Springer International Publishing, 2016.

[35] Triantafillou, Eleni, Hugo Larochelle, Jake Snell, Josh Tenenbaum, Kevin Jordan Swersky, Mengye Ren, Richard Zemel, and Sachin Ravi. Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR 2018.

[36] Rabinowitz, Neil C., Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Eslami, and Matthew Botvinick. Machine Theory of Mind. arXiv preprint arXiv:1802.07740 (2018).

[37] Reed, Scott, Yutian Chen, Thomas Paine, Aron van den Oord, S. M. Eslami, Danilo Rezende, Oriol Vinyals, and Nando de Freitas. Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. arXiv preprint arXiv:1710.10304 (2017).

[38] Xu, Zhongwen, Hado van Hasselt, and David Silver. Meta-Gradient Reinforcement Learning arXiv preprint arXiv:1805.09801 (2018).

[39] Xu, Kelvin, Ellis Ratner, Anca Dragan, Sergey Levine, and Chelsea Finn. Learning a Prior over Intent via Meta-Inverse Reinforcement Learning arXiv preprint arXiv:1805.12573 (2018).

[40] Finn, Chelsea, Kelvin Xu, and Sergey Levine. Probabilistic Model-Agnostic Meta-Learning arXiv preprint arXiv:1806.02817 (2018).

[41] Gupta, Abhishek, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine. Unsupervised Meta-Learning for Reinforcement Learning arXiv preprint arXiv:1806.04640(2018).

[42] Yoon, Sung Whan, Jun Seo, and Jaekyun Moon. Meta Learner with Linear Nulling arXiv preprint arXiv:1806.01010 (2018).

[43] Kim, Taesup, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn. Bayesian Model-Agnostic Meta-Learning arXiv preprint arXiv:1806.03836 (2018).

[44] Gupta, Abhishek, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine. Meta-Reinforcement Learning of Structured Exploration Strategies arXiv preprint arXiv:1802.07245 (2018).

[45] Clavera, Ignasi, Anusha Nagabandi, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn. Learning to Adapt: Meta-Learning for Model-Based Control arXiv preprint arXiv:1803.11347 (2018).

[46] Houthooft, Rein, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel. Evolved policy gradients arXiv preprint arXiv:1802.04821 (2018).

[47] Xu, Tianbing, Qiang Liu, Liang Zhao, Wei Xu, and Jian Peng. Learning to Explore with Meta-Policy Gradient arXiv preprint arXiv:1803.05044 (2018).

[48] Stadie, Bradly C., Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Ilya Sutskever. Some considerations on learning to explore via meta-reinforcement learning arXiv preprint arXiv:1803.01118 (2018).

[49] Luca Bertinetto, Joao F. Henriques, Philip Torr and Andrea Vedaldi. Meta-learning with differentiable closed-form solvers arXiv preprint arXiv:1805.08136 (2018).

[50] Yoonho Lee, Seungjin Choi. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace. ICML 2018.

Popular Learning Projects
Popular Meta Learning Projects
Popular Learning Resources Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Deep Learning
Meta Learning