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[1] G. Brier. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .
[2] Roland Vollgraf,et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.
[3] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[4] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .
[5] Ruslan Salakhutdinov,et al. Breaking the Softmax Bottleneck: A High-Rank RNN Language Model , 2017, ICLR.
[6] Alexander A. Alemi,et al. Deep Variational Information Bottleneck , 2017, ICLR.
[7] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[8] Shachar Fleishman,et al. Novelty Detection with GAN , 2018, ArXiv.
[9] Alexander A. Alemi,et al. Fixing a Broken ELBO , 2017, ICML.
[10] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[11] Naftali Tishby,et al. Deep learning and the information bottleneck principle , 2015, 2015 IEEE Information Theory Workshop (ITW).
[12] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[13] Graham W. Taylor,et al. Learning Confidence for Out-of-Distribution Detection in Neural Networks , 2018, ArXiv.
[14] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[15] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[16] Kibok Lee,et al. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples , 2017, ICLR.
[17] Jascha Sohl-Dickstein,et al. Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks , 2018, ICML.
[18] Arild Nøkland,et al. Shifting Mean Activation Towards Zero with Bipolar Activation Functions , 2017, ICLR.
[19] Stefano Soatto,et al. Emergence of invariance and disentangling in deep representations , 2017 .
[20] Nathan Srebro,et al. The Marginal Value of Adaptive Gradient Methods in Machine Learning , 2017, NIPS.