Reconciling meta-learning and continual learning with online mixtures of tasks
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Thomas L. Griffiths | Ghassen Jerfel | Erin Grant | Katherine Heller | T. Griffiths | K. Heller | Erin Grant | Ghassen Jerfel
[1] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[2] Bernhard Schölkopf,et al. Discriminative k-shot learning using probabilistic models , 2017, ArXiv.
[3] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[4] D. Sculley,et al. Web-scale k-means clustering , 2010, WWW '10.
[5] David Barber,et al. Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting , 2018, NeurIPS.
[6] Anton Schwaighofer,et al. Learning Gaussian processes from multiple tasks , 2005, ICML.
[7] Thomas L. Griffiths,et al. Recasting Gradient-Based Meta-Learning as Hierarchical Bayes , 2018, ICLR.
[8] M. Escobar,et al. Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .
[9] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[10] Tsendsuren Munkhdalai,et al. Metalearning with Hebbian Fast Weights , 2018, ArXiv.
[11] Zoubin Ghahramani,et al. Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.
[13] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[14] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[15] Alexis Boukouvalas,et al. What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm , 2016, PloS one.
[16] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[17] Jürgen Schmidhuber,et al. Neural Expectation Maximization , 2017, NIPS.
[18] Neil D. Lawrence,et al. Learning to learn with the informative vector machine , 2004, ICML.
[19] Adam J Rothman,et al. Sparse Multivariate Regression With Covariance Estimation , 2010, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.
[20] Anne G E Collins,et al. Cognitive control over learning: creating, clustering, and generalizing task-set structure. , 2013, Psychological review.
[21] H. Robbins. A Stochastic Approximation Method , 1951 .
[22] Jonathan Baxter,et al. A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling , 1997, Machine Learning.
[23] Samuel J. Gershman,et al. A Tutorial on Bayesian Nonparametric Models , 2011, 1106.2697.
[24] Svetha Venkatesh,et al. Factorial Multi-Task Learning : A Bayesian Nonparametric Approach , 2013, ICML.
[25] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[26] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[27] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[28] Thomas G. Dietterich,et al. To transfer or not to transfer , 2005, NIPS 2005.
[29] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[30] Max Welling,et al. Bayesian k-Means as a Maximization-Expectation Algorithm , 2009, Neural Computation.
[31] Nitish Srivastava,et al. Discriminative Transfer Learning with Tree-based Priors , 2013, NIPS.
[32] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[33] Tom Heskes,et al. Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach , 1998, ICML.
[34] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[35] Surya Ganguli,et al. Continual Learning Through Synaptic Intelligence , 2017, ICML.
[36] Shannon L. Risacher,et al. Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer's disease , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[37] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[38] Sergey Levine,et al. Probabilistic Model-Agnostic Meta-Learning , 2018, NeurIPS.
[39] Jonathan Baxter,et al. A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..
[40] Joan Bruna,et al. Few-Shot Learning with Graph Neural Networks , 2017, ICLR.
[41] Peter Müller,et al. Issues in Bayesian Analysis of Neural Network Models , 1998, Neural Computation.
[42] Rich Caruana,et al. Multitask Learning: A Knowledge-Based Source of Inductive Bias , 1993, ICML.
[43] Sebastian Nowozin,et al. Meta-Learning Probabilistic Inference for Prediction , 2018, ICLR.
[44] E. L. Lehmann,et al. Theory of point estimation , 1950 .
[45] Sebastian Thrun,et al. Discovering Structure in Multiple Learning Tasks: The TC Algorithm , 1996, ICML.
[46] Hal Daumé,et al. Bayesian Multitask Learning with Latent Hierarchies , 2009, UAI.
[47] Jeff G. Schneider,et al. Learning Multiple Tasks with a Sparse Matrix-Normal Penalty , 2010, NIPS.
[48] Yoshua Bengio,et al. The effects of negative adaptation in Model-Agnostic Meta-Learning , 2018, ArXiv.
[49] J. Besag. On the Statistical Analysis of Dirty Pictures , 1986 .
[50] Jürgen Schmidhuber,et al. Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks , 1992, Neural Computation.
[51] N. Metropolis,et al. The Monte Carlo method. , 1949 .
[52] Radford M. Neal,et al. A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model , 2004 .
[53] Seungjin Choi,et al. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace , 2018, ICML.
[54] Lawrence Carin,et al. Multi-Task Learning for Classification with Dirichlet Process Priors , 2007, J. Mach. Learn. Res..
[55] Richard E. Turner,et al. Variational Continual Learning , 2017, ICLR.
[56] Hong Yu,et al. Meta Networks , 2017, ICML.
[57] Nikos Komodakis,et al. Dynamic Few-Shot Visual Learning Without Forgetting , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[58] Dahua Lin,et al. Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation , 2013, NIPS.
[59] Jiawei Han,et al. Knowledge transfer via multiple model local structure mapping , 2008, KDD.
[60] Tom Heskes,et al. Task Clustering and Gating for Bayesian Multitask Learning , 2003, J. Mach. Learn. Res..
[61] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[62] Raquel Urtasun,et al. Few-Shot Learning Through an Information Retrieval Lens , 2017, NIPS.
[63] Pieter Abbeel,et al. A Simple Neural Attentive Meta-Learner , 2017, ICLR.
[64] Carl E. Rasmussen,et al. The Infinite Gaussian Mixture Model , 1999, NIPS.
[65] Emily B. Fox,et al. Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data , 2012, NIPS.
[66] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[67] Alexandre Lacoste,et al. TADAM: Task dependent adaptive metric for improved few-shot learning , 2018, NeurIPS.
[68] Emily B. Fox,et al. Streaming Variational Inference for Bayesian Nonparametric Mixture Models , 2014, AISTATS.
[69] Joshua B. Tenenbaum,et al. Learning with Hierarchical-Deep Models , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[70] Dit-Yan Yeung,et al. A Regularization Approach to Learning Task Relationships in Multitask Learning , 2014, ACM Trans. Knowl. Discov. Data.
[71] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.