2017 ModelAgnosticMetaLearningforFas

From GM-RKB
(Redirected from Finn et al., 2017)
Jump to navigation Jump to search

Subject Headings: Neural Meta-Learning.

Notes

Cited By

2018

Quotes

Abstract

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

References

  • 1. Abadi, Martín, Agarwal, Ashish, Barham, Paul, Brevdo, Eugene, Chen, Zhifeng, Citro, Craig, Corrado, Greg S, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Et Al. Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems. arXiv Preprint ArXiv:1603.04467, 2016.
  • 2. Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando De Freitas, Learning to Learn by Gradient Descent by Gradient Descent, Proceedings of the 30th International Conference on Neural Information Processing Systems, p.3988-3996, December 05-10, 2016, Barcelona, Spain
  • 3. Bengio, Samy, Bengio, Yoshua, Cloutier, Jocelyn, and Gecsei, Jan. On the Optimization of a Synaptic Learning Rule. In Optimality in Artificial and Biological Neural Networks, Pp. 6-8, 1992.
  • 4. Bengio, Yoshua, Bengio, Samy, and Cloutier, Jocelyn. Learning a Synaptic Learning Rule. Université De Montréal, Département D'informatique Et De Recherche Opérationnelle, 1990.
  • 5. Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell, DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition, Proceedings of the 31st International Conference on International Conference on Machine Learning, June 21-26, 2014, Beijing, China
  • 6. Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel, Benchmarking Deep Reinforcement Learning for Continuous Control, Proceedings of the 33rd International Conference on International Conference on Machine Learning, June 19-24, 2016, New York, NY, USA
  • 7. Duan, Yan, Schulman, John, Chen, Xi, Bartlett, Peter L, Sutskever, Ilya, and Abbeel, Pieter. Rl2: Fast Reinforcement Learning via Slow Reinforcement Learning. arXiv Preprint ArXiv:1611.02779, 2016b.
  • 8. Edwards, Harrison and Storkey, Amos. Towards a Neural Statistician. International Conference on Learning Representations (ICLR), 2017.
  • 9. Goodfellow, Ian J, Shlens, Jonathon, and Szegedy, Christian. Explaining and Harnessing Adversarial Examples. International Conference on Learning Representations (ICLR), 2015.
  • 10. Ha, David, Dai, Andrew, and Le, Quoc V. Hypernetworks. International Conference on Learning Representations (ICLR), 2017.
  • 11. Sepp Hochreiter, A. Steven Younger, Peter R. Conwell, Learning to Learn Using Gradient Descent, Proceedings of the International Conference on Artificial Neural Networks, p.87-94, August 21-25, 2001
  • 12. Fast Learning for Problem Classes Using Knowledge Based Network Initialization, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, p.6619, July 24-27, 2000
  • 13. Sergey Ioffe, Christian Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 06-11, 2015, Lille, France
  • 14. Kaiser, Lukasz, Nachum, Ofir, Roy, Aurko, and Bengio, Samy. Learning to Remember Rare Events. International Conference on Learning Representations (ICLR), 2017.
  • 15. Kingma, Diederik and Ba, Jimmy. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR), 2015.
  • 16. Kirkpatrick, James, Pascanu, Razvan, Rabinowitz, Neil, Veness, Joel, Desjardins, Guillaume, Rusu, Andrei A, Milan, Kieran, Quan, John, Ramalho, Tiago, Grabska-Barwinska, Agnieszka, Et Al. Overcoming Catastrophic Forgetting in Neural Networks. arXiv Preprint ArXiv:1612.00796, 2016.
  • 17. Koch, Gregory. Siamese Neural Networks for One-shot Image Recognition. ICML Deep Learning Workshop, 2015.
  • 18. Krähenbuhl, Philipp, Doersch, Carl, Donahue, Jeff, and Darrell, Trevor. Data-dependent Initializations of Convolutional Neural Networks. International Conference on Learning Representations (ICLR), 2016.
  • 19. Lake, Brenden M, Salakhutdinov, Ruslan, Gross, Jason, and Tenenbaum, Joshua B. One Shot Learning of Simple Visual Concepts. In Conference of the Cognitive Science Society (CogSci), 2011.
  • 20. Li, Ke and Malik, Jitendra. Learning to Optimize. International Conference on Learning Representations (ICLR), 2017.
  • 21. Dougal Maclaurin, David Duvenaud, Ryan P. Adams, Gradient-based Hyperparameter Optimization through Reversible Learning, Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 06-11, 2015, Lille, France
  • 22. Munkhdalai, Tsendsuren and Yu, Hong. Meta Networks. International Conferecence on Machine Learning (ICML), 2017.
  • 23. Naik, Devang K and Mammone, RJ. Meta-neural Networks That Learn by Learning. In International Joint Conference on Neural Networks (IJCNN), 1992.
  • 24. Parisotto, Emilio, Ba, Jimmy Lei, and Salakhutdinov, Ruslan. Actor-mimic: Deep Multitask and Transfer Reinforcement Learning. International Conference on Learning Representations (ICLR), 2016.
  • 25. Ravi, Sachin and Larochelle, Hugo. Optimization As a Model for Few-shot Learning. In International Conference on Learning Representations (ICLR), 2017.
  • 26. Rei, Marek. Online Representation Learning in Recurrent Neural Language Models. arXiv Preprint ArXiv:1508.03854, 2015.
  • 27. Danilo J. Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra, One-shot Generalization in Deep Generative Models, Proceedings of the 33rd International Conference on International Conference on Machine Learning, June 19-24, 2016, New York, NY, USA
  • 28. Tim Salimans, Diederik P. Kingma, Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks, Proceedings of the 30th International Conference on Neural Information Processing Systems, p.901-909, December 05-10, 2016, Barcelona, Spain
  • 29. Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap, Meta-learning with Memory-augmented Neural Networks, Proceedings of the 33rd International Conference on International Conference on Machine Learning, June 19-24, 2016, New York, NY, USA
  • 30. Saxe, Andrew, McClelland, James, and Ganguli, Surya. Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks. International Conference on Learning Representations (ICLR), 2014.
  • 31. Schmidhuber, Jurgen. Evolutionary Principles in Self-referential Learning. On Learning how to Learn: The Meta-meta-... Hook.) Diploma Thesis, Institut F. Informatik, Tech. Univ. Munich, 1987.
  • 32. Jürgen Schmidhuber, Learning to Control Fast-weight Memories: An Alternative to Dynamic Recurrent Networks, Neural Computation, v.4 n.1, p.131-139, Jan. 1992
  • 33. John Schulman, Sergey Levine, Philipp Moritz, Michael Jordan, Pieter Abbeel, Trust Region Policy Optimization, Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 06-11, 2015, Lille, France
  • 34. Shyam, Pranav, Gupta, Shubham, and Dukkipati, Ambedkar. Attentive Recurrent Comparators. International Conferecence on Machine Learning (ICML), 2017.
  • 35. Snell, Jake, Swersky, Kevin, and Zemel, Richard S. Prototypical Networks for Few-shot Learning. arXiv Preprint ArXiv:1703.05175, 2017.
  • 36. Sebastian Thrun, Lorien Pratt, Learning to Learn, Kluwer Academic Publishers, Norwell, MA, 1998
  • 37. Todorov, Emanuel, Erez, Tom, and Tassa, Yuval. Mujoco: A Physics Engine for Model-based Control. In International Conference on Intelligent Robots and Systems (IROS), 2012.
  • 38. Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra, Matching Networks for One Shot Learning, Proceedings of the 30th International Conference on Neural Information Processing Systems, p.3637-3645, December 05-10, 2016, Barcelona, Spain
  • 39. Wang, Jane X, Kurth-Nelson, Zeb, Tirumala, Dhruva, Soyer, Hubert, Leibo, Joel Z, Munos, Remi, Blundell, Charles, Kumaran, Dharshan, and Botvinick, Matt. Learning to Reinforcement Learn. arXiv Preprint ArXiv:1611.05763, 2016.
  • 40. Ronald J. Williams, Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Machine Learning, v.8 n.3-4, p.229-256, May 1992

}};


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2017 ModelAgnosticMetaLearningforFasPieter Abbeel
Chelsea Finn
Sergey Levine		Model-agnostic Meta-learning for Fast Adaptation of Deep Networks
Retrieved from "http://www.gabormelli.com/RKB/index.php?title=2017_ModelAgnosticMetaLearningforFas&oldid=812234"