Meta-Learning with Warped Gradient Descent
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[1] Martha White,et al. Meta-Learning Representations for Continual Learning , 2019, NeurIPS.
[2] Subhransu Maji,et al. Meta-Learning With Differentiable Convex Optimization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] S. Levine,et al. Guided Meta-Policy Search , 2019, NeurIPS.
[4] Sergey Levine,et al. Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables , 2019, ICML.
[5] Junier B. Oliva,et al. Meta-Curvature , 2019, NeurIPS.
[6] Amos J. Storkey,et al. How to train your MAML , 2018, ICLR.
[7] Katja Hofmann,et al. Fast Context Adaptation via Meta-Learning , 2018, ICML.
[8] Kenneth O. Stanley,et al. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity , 2018, ICLR.
[9] Neil D. Lawrence,et al. Transferring Knowledge across Learning Processes , 2018, ICLR.
[10] Razvan Pascanu,et al. Meta-Learning with Latent Embedding Optimization , 2018, ICLR.
[11] Jascha Sohl-Dickstein,et al. Meta-Learning Update Rules for Unsupervised Representation Learning , 2018, ICLR.
[12] Alexandre Lacoste,et al. Uncertainty in Multitask Transfer Learning , 2018, ArXiv.
[13] Yoshua Bengio,et al. Bayesian Model-Agnostic Meta-Learning , 2018, NeurIPS.
[14] Alexandre Lacoste,et al. TADAM: Task dependent adaptive metric for improved few-shot learning , 2018, NeurIPS.
[15] Hujun Yin,et al. Breaking the Activation Function Bottleneck through Adaptive Parameterization , 2018, NeurIPS.
[16] Nikos Komodakis,et al. Dynamic Few-Shot Visual Learning Without Forgetting , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Kenneth O. Stanley,et al. Differentiable plasticity: training plastic neural networks with backpropagation , 2018, ICML.
[18] Joshua Achiam,et al. On First-Order Meta-Learning Algorithms , 2018, ArXiv.
[19] Sanjeev Arora,et al. On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization , 2018, ICML.
[20] Joshua B. Tenenbaum,et al. Meta-Learning for Semi-Supervised Few-Shot Classification , 2018, ICLR.
[21] Renjie Liao,et al. Understanding Short-Horizon Bias in Stochastic Meta-Optimization , 2018, ICLR.
[22] Bin Wu,et al. Deep Meta-Learning: Learning to Learn in the Concept Space , 2018, ArXiv.
[23] Thomas L. Griffiths,et al. Recasting Gradient-Based Meta-Learning as Hierarchical Bayes , 2018, ICLR.
[24] Seungjin Choi,et al. Meta-Learning with Adaptive Layerwise Metric and Subspace , 2018, ArXiv.
[25] Aaron C. Courville,et al. FiLM: Visual Reasoning with a General Conditioning Layer , 2017, AAAI.
[26] Pieter Abbeel,et al. A Simple Neural Attentive Meta-Learner , 2017, ICLR.
[27] Wei Shen,et al. Few-Shot Image Recognition by Predicting Parameters from Activations , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Tsendsuren Munkhdalai,et al. Learning Rapid-Temporal Adaptations , 2017, ArXiv.
[30] Hang Li,et al. Meta-SGD: Learning to Learn Quickly for Few Shot Learning , 2017, ArXiv.
[31] Angelika Steger,et al. Fast-Slow Recurrent Neural Networks , 2017, NIPS.
[32] Andrea Vedaldi,et al. Learning multiple visual domains with residual adapters , 2017, NIPS.
[33] Byoung-Tak Zhang,et al. Overcoming Catastrophic Forgetting by Incremental Moment Matching , 2017, NIPS.
[34] Kaiming He,et al. Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[35] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[36] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[37] Andrea Vedaldi,et al. Universal representations: The missing link between faces, text, planktons, and cat breeds , 2017, ArXiv.
[38] Zeb Kurth-Nelson,et al. Learning to reinforcement learn , 2016, CogSci.
[39] Misha Denil,et al. Learning to Learn without Gradient Descent by Gradient Descent , 2016, ICML.
[40] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[41] Quoc V. Le,et al. HyperNetworks , 2016, ICLR.
[42] Jitendra Malik,et al. Learning to Optimize , 2016, ICLR.
[43] Joseph Suarez,et al. Language Modeling with Recurrent Highway Hypernetworks , 2017, NIPS.
[44] Geoffrey E. Hinton,et al. Using Fast Weights to Attend to the Recent Past , 2016, NIPS.
[45] Luca Bertinetto,et al. Learning feed-forward one-shot learners , 2016, NIPS.
[46] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[47] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[48] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Jorge Nocedal,et al. A Stochastic Quasi-Newton Method for Large-Scale Optimization , 2014, SIAM J. Optim..
[50] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[51] Razvan Pascanu,et al. Natural Neural Networks , 2015, NIPS.
[52] Roger B. Grosse,et al. Optimizing Neural Networks with Kronecker-factored Approximate Curvature , 2015, ICML.
[53] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[54] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[55] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[56] Razvan Pascanu,et al. Revisiting Natural Gradient for Deep Networks , 2013, ICLR.
[57] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[58] Joshua B. Tenenbaum,et al. One shot learning of simple visual concepts , 2011, CogSci.
[59] James Martens,et al. Deep learning via Hessian-free optimization , 2010, ICML.
[60] Fei-Fei Li,et al. ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Hans-Georg Zimmermann,et al. Recurrent Neural Networks Are Universal Approximators , 2006, ICANN.
[62] Marc Teboulle,et al. Mirror descent and nonlinear projected subgradient methods for convex optimization , 2003, Oper. Res. Lett..
[63] John M. Lee. Introduction to Smooth Manifolds , 2002 .
[64] Sepp Hochreiter,et al. Learning to Learn Using Gradient Descent , 2001, ICANN.
[65] R. French. Catastrophic forgetting in connectionist networks , 1999, Trends in Cognitive Sciences.
[66] Stephen J. Wright,et al. Numerical Optimization , 2018, Fundamental Statistical Inference.
[67] Sebastian Thrun,et al. Learning to Learn: Introduction and Overview , 1998, Learning to Learn.
[68] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[69] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[70] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[71] Jürgen Schmidhuber,et al. Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks , 1992, Neural Computation.
[72] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[73] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[74] Geoffrey E. Hinton. Using fast weights to deblur old memories , 1987 .