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Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model.
Towards Open Intent Discovery for Conversational Text
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An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
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How Can Deep Rectifier Networks Achieve Linear Separability and Preserve Distances?
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Sorting Out Lipschitz Function Approximation
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Invertible Residual Networks
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Using Pre-Training Can Improve Model Robustness and Uncertainty
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Calibrating Deep Convolutional Gaussian Processes
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A family of algorithms for approximate bayesian inference
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
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J. van Amersfoort, L. Smith, Y. W. Teh, and Y. Gal · 2003
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Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
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Random Projection, Margins, Kernels, and Feature-Selection
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Local Distance Preservation in the GP-LVM Through Back Constraints
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Gaussian Processes for Machine Learning
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Metric Spaces
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Probabilistic forecasts, calibration and sharpness
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Strictly Proper Scoring Rules, Prediction, and Estimation
T. Gneiting and A. E. Raftery · 2007
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Classification with a Reject Option using a Hinge Loss
P. L. Bartlett and M. H. Wegkamp · 2008
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Random Features for Large-Scale Kernel Machines
A. Rahimi and B. Recht · 2008
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Reliability, sufficiency, and the decomposition of proper scores
J. Bröcker · 2009
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Learning Non-Linear Combinations of Kernels
C. Cortes, M. Mohri, and A. Rostamizadeh · 2009
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Reading Digits in Natural Images with Unsupervised Feature Learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Proper local scoring rules
M. Parry, A. P. Dawid, and S. Lauritzen · 2012
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NICE: Non-linear Independent Components Estimation
L. Dinh, D. Krueger, and Y. Bengio · 2014
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Probability Models for Open Set Recognition
W. J. Scheirer, L. P. Jain, and T. E. Boult · 2014
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Probabilism, entropies and strictly proper scoring rules
J. Landes · 2015
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Principled Uncertainty Estimation for Deep Neural Networks, 2018
R. E. Harang and E. M. Rudd · 2018
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
D. Hendrycks and T. Dietterich · 2018
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Excessive Invariance Causes Adversarial Vulnerability
J.-H. Jacobsen, J. Behrmann, R. Zemel, and M. Bethge · 2018
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i-RevNet: Deep Invertible Networks
r.-H. Jacobsen, A. W. M. Smeulders, and E. Oyallon · 2018
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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
K. Lee, H. Lee, K. Lee, and J. Shin · 2018
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Predictive Uncertainty Estimation via Prior Networks
A. Malinin and M. Gales · 2018
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Finite Sample Bernstein von Mises Theorem for Semiparametric Problems
M. Panov and V. Spokoiny · 2015
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Scalable Bayesian Optimization Using Deep Neural Networks
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. M. A. Patwary, Prabhat, and R. P. Adams · 2015
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Towards Open Set Deep Networks
A. Bendale and T. E. Boult · 2016
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Manifold Gaussian Processes for regression
R. Calandra, J. Peters, C. E. Rasmussen, and M. P. Deisenroth · 2016
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Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
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Stochastic Variational Deep Kernel Learning
A. G. Wilson, Z. Hu, R. Salakhutdinov, and E. P. Xing · 2016
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Prior Networks for Detection of Adversarial Attacks
A. Malinin and M. Gales · 2018
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Spectral Normalization for Generative Adversarial Networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme, G. Tucker, and J. Snoek · 2018
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A Scalable Laplace Approximation for Neural Networks
H. Ritter, A. Botev, and D. Barber · 2018
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Reachability analysis of deep neural networks with provable guarantees
W. Ruan, X. Huang, and M. Kwiatkowska · 2018
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Evidential Deep Learning to Quantify Classification Uncertainty
M. Sensoy, L. Kaplan, and M. Kandemir · 2018
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Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Y. Tsuzuku, I. Sato, and M. Sugiyama · 2018
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Challenges for Toxic Comment Classification: An In-Depth Error Analysis
B. van Aken, J. Risch, R. Krestel, and A. Loser · 2018
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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
T.-W. Weng, H. Zhang, P.-Y. Chen, J. Yi, D. Su, Y. Gao, C.-J. Hsieh, and L. Daniel · 2018
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A deterministic and computable Bernstein-von Mises theorem
G. P. Dehaene · 2019
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Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem
M. Hein, M. Andriushchenko, and J. Bitterwolf · 2019
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Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
J.-H. Jacobsen, J. Behrmannn, N. Carlini, F. TramÚr, and N. Papernot · 2019
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Approximate Inference Turns Deep Networks into Gaussian Processes
M. E. E. Khan, A. Immer, E. Abedi, and M. Korzepa · 2019
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Measuring calibration in deep learning
J. Nixon, M. W. Dusenberry, L. Zhang, G. Jerfel, and D. Tran · 2019
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Single-Model Uncertainties for Deep Learning
N. Tagasovska and D. Lopez-Paz · 2019
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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors
M. Dusenberry, G. Jerfel, Y. Wen, Y. Ma, J. Snoek, K. Heller, B. Lakshminarayanan, and D. Tran · 2020
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AugMix: A Simple Method to Improve Robustness and Uncertainty under Data Shift
D. Hendrycks*, N. Mu*, E. D. Cubuk, B. Zoph, J. Gilmer, and B. Lakshminarayanan · 2020
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Towards neural networks that provably know when they don’t know
A. Meinke and M. Hein · 2020
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Residual Networks as Flows of Diffeomorphisms
F. Rousseau, L. Drumetz, and R. Fablet · 2020
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BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning
Y. Wen, D. Tran, and J. Ba · 2020
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User Utterance Acquisition for Training Task-Oriented Bots: A Review of Challenges, Techniques and Opportunities
M.-A. Yaghoub-Zadeh-Fard, B. Benatallah, F. Casati, M. Chai Barukh, and S. Zamanirad · 2020
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Out-of-Domain Detection for Natural Language Understanding in Dialog Systems
Y. Zheng, G. Chen, and M. Huang · 2020
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