暂无分享,去创建一个
[1] Alexander A. Alemi,et al. Deep Variational Information Bottleneck , 2017, ICLR.
[2] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[3] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[4] Hang Li,et al. Meta-SGD: Learning to Learn Quickly for Few Shot Learning , 2017, ArXiv.
[5] Swami Sankaranarayanan,et al. MetaReg: Towards Domain Generalization using Meta-Regularization , 2018, NeurIPS.
[6] Katja Hofmann,et al. Fast Context Adaptation via Meta-Learning , 2018, ICML.
[7] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[8] Alexandre Lacoste,et al. TADAM: Task dependent adaptive metric for improved few-shot learning , 2018, NeurIPS.
[9] Christopher D. Manning,et al. Fast dropout training , 2013, ICML.
[10] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[11] Razvan Pascanu,et al. Meta-Learning with Latent Embedding Optimization , 2018, ICLR.
[12] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[13] Ariel D. Procaccia,et al. Variational Dropout and the Local Reparameterization Trick , 2015, NIPS.
[14] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[15] Subhransu Maji,et al. Meta-Learning With Differentiable Convex Optimization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[17] Pieter Abbeel,et al. A Simple Neural Attentive Meta-Learner , 2017, ICLR.
[18] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[19] Naftali Tishby,et al. Deep learning and the information bottleneck principle , 2015, 2015 IEEE Information Theory Workshop (ITW).
[20] Quoc V. Le,et al. DropBlock: A regularization method for convolutional networks , 2018, NeurIPS.
[21] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[22] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[23] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[24] Seungjin Choi,et al. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace , 2018, ICML.
[25] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[26] Thomas Brox,et al. Inverting Visual Representations with Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[28] Alex Beatson,et al. Amortized Bayesian Meta-Learning , 2018, ICLR.
[29] Aleksander Madry,et al. How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift) , 2018, NeurIPS.
[30] Ioannis Mitliagkas,et al. Manifold Mixup: Learning Better Representations by Interpolating Hidden States , 2018, 1806.05236.
[31] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[32] Bernt Schiele,et al. Disentangling Adversarial Robustness and Generalization , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Stefano Soatto,et al. Information Dropout: Learning Optimal Representations Through Noisy Computation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Stefano Soatto,et al. Entropy-SGD: biasing gradient descent into wide valleys , 2016, ICLR.
[35] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[36] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[37] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[38] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[39] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[40] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[41] Dan Boneh,et al. Adversarial Training and Robustness for Multiple Perturbations , 2019, NeurIPS.
[42] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[43] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[44] Joshua Achiam,et al. On First-Order Meta-Learning Algorithms , 2018, ArXiv.
[45] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[46] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[47] Brendan J. Frey,et al. Adaptive dropout for training deep neural networks , 2013, NIPS.
[48] Sergey Levine,et al. Probabilistic Model-Agnostic Meta-Learning , 2018, NeurIPS.
[49] Alex Kendall,et al. Concrete Dropout , 2017, NIPS.
[50] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[51] Eunho Yang,et al. Uncertainty-Aware Attention for Reliable Interpretation and Prediction , 2018, NeurIPS.