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Brian C. Lovell | Can Peng | Sam Maksoud | Kun Zhao | B. Lovell | Can Peng | Kun Zhao | Sam Maksoud
[1] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[2] Razvan Pascanu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[3] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[4] Jihoon Kim,et al. Calibrating predictive model estimates to support personalized medicine , 2011, J. Am. Medical Informatics Assoc..
[5] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[6] Roland Vollgraf,et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.
[7] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[8] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[9] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[10] Didrik Nielsen,et al. Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam , 2018, ICML.
[11] Marcus Liwicki,et al. A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference , 2019, ArXiv.
[12] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[14] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[15] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[16] Alex Graves,et al. Practical Variational Inference for Neural Networks , 2011, NIPS.
[17] Mohammad Emtiyaz Khan,et al. Practical Deep Learning with Bayesian Principles , 2019, NeurIPS.
[18] Richard E. Turner,et al. Variational Continual Learning , 2017, ICLR.
[19] Hanna Tseran. Natural Variational Continual Learning , 2018 .
[20] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[21] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[22] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[23] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.