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Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems.
Uncertainty-based out-of-distribution detection in deep reinforcement learning
Sedlmeier, A., Gabor, T., Phan, T., Belzner, L., and Linnhoff-Popien, C. (2019) · 1901
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Learning from delayed rewards
Watkins, C. J. C. H. (1989) · 1989
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Bootstrap Methods: Another Look at the Jackknife
Efron, B. (1992) · 1992
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A practical bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · 1992
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Novelty detection and neural network validation
Bishop, C. M. (1994) · 1994
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Bayesian error bars for regression
Qazaz, C. S. (1996) · 1996
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Introduction to reinforcement learning
Sutton, R. S. and Barto, A. G. (1998) · 1998
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Pattern recognition and machine learning
Bishop, C. M. (2006) · 2006
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Notmnist dataset
Bulatov, Y. (2011) · 2011
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Practical variational inference for neural networks
Graves, A. (2011) · 2011
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A review of novelty detection
Pimentel, M. A., Clifton, D. A., Clifton, L., and Tarassenko, L. (2014) · 2014
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Markov decision processes: discrete stochastic dynamic programming
Puterman, M. L. (2014) · 2014
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Weight Uncertainty in Neural Networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015) · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · 2015
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Concrete Problems in AI Safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D. (2016) · 2016
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Openai gym
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016) · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z. (2016) · 2016
Concrete dropout
Gal, Y., Hron, J., and Kendall, A. (2017) · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
Kahn, G., Villaflor, A., Pong, V., Abbeel, P., and Levine, S. (2017) · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y. (2017) · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 2017
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Leveraging uncertainty information from deep neural networks for disease detection
Leibig, C., Allken, V., Ayhan, M. S., Berens, P., and Wahl, S. (2017) · 2017
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Dropout Inference in Bayesian Neural Networks with Alpha-divergences
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D. and Gimpel, K. (2016) · 2016
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Black-box α \alpha -divergence minimization
Hernández-Lobato, J., Li, Y., Rowland, M., Hernández-Lobato, D., Bui, T., and Ttarner, R. (2016) · 2016
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Deep exploration via bootstrapped dqn
Osband, I., Blundell, C., Pritzel, A., and Van Roy, B. (2016) · 2016
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A distributional perspective on reinforcement learning
Bellemare, M. G., Dabney, W., and Munos, R. (2017) · 2017
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Li, Y. and Gal, Y. (2017) · 2017
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Liang, S., Li, Y., and Srikant, R. (2017) · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G. (2017) · 2017
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The power of ensembles for active learning in image classification
Beluch, W. H., Genewein, T., Nürnberger, A., and Köhler, J. M. (2018) · 2018
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Randomized Prior Functions for Deep Reinforcement Learning
Osband, I., Aslanides, J., and Cassirer, A. (2018) · 2018
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