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Out-of-Distribution (OoD) detection is important for building safe artificial intelligence systems.
On the Inductive Bias of Neural Tangent Kernels
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Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
Hein, M.; and Andriushchenko, M. 2017 · 2017
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
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Invertible Residual Networks
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Deep Anomaly Detection with Outlier Exposure
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Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
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Deep Learning for Classical Japanese Literature
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Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Rabanser, S.; Günnemann, S.; and Lipton, Z. 2019 · 2019
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Likelihood Ratios for Out-of-Distribution Detection
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Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks
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Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models
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