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[1] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[2] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[3] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[4] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[5] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[6] Erich Elsen,et al. Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.
[7] Alex Krizhevsky,et al. One weird trick for parallelizing convolutional neural networks , 2014, ArXiv.
[8] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[9] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[10] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[11] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[12] Milos Hauskrecht,et al. Obtaining Well Calibrated Probabilities Using Bayesian Binning , 2015, AAAI.
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] Johannes Gehrke,et al. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.
[15] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[17] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[18] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[19] John Schulman,et al. Concrete Problems in AI Safety , 2016, ArXiv.
[20] Yarin Gal,et al. Dropout Inference in Bayesian Neural Networks with Alpha-divergences , 2017, ICML.
[21] Fan Yang,et al. Good Semi-supervised Learning That Requires a Bad GAN , 2017, NIPS.
[22] R. Srikant,et al. Principled Detection of Out-of-Distribution Examples in Neural Networks , 2017, ArXiv.
[23] Yingyu Liang,et al. Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.
[24] Xi Chen,et al. PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications , 2017, ICLR.
[25] Tomas Pfister,et al. Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[27] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[28] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[29] Jinwoo Shin,et al. Confident Multiple Choice Learning , 2017, ICML.
[30] Max Welling,et al. Multiplicative Normalizing Flows for Variational Bayesian Neural Networks , 2017, ICML.
[31] Mark J. F. Gales,et al. Incorporating Uncertainty into Deep Learning for Spoken Language Assessment , 2017, ACL.
[32] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[33] Ruben Villegas,et al. Learning to Generate Long-term Future via Hierarchical Prediction , 2017, ICML.
[34] Yi Yang,et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[35] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.