暂无分享,去创建一个
Byron Boots | Amirreza Shaban | Ching-An Cheng | Richard Hartley | Amir Rahimi | Amir M. Rahimi | R. Hartley | Byron Boots | Amirreza Shaban | Ching-An Cheng
[1] Jihoon Kim,et al. Calibrating predictive model estimates to support personalized medicine , 2011, J. Am. Medical Informatics Assoc..
[2] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[3] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Tengyu Ma,et al. Verified Uncertainty Calibration , 2019, NeurIPS.
[5] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[6] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[7] Dong Wang,et al. Learning to Navigate for Fine-grained Classification , 2018, ECCV.
[8] Peter A. Flach,et al. Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers , 2017, AISTATS.
[9] Seong Joon Oh,et al. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Bohyung Han,et al. Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Thomas B. Moeslund,et al. Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[12] C. W. Clenshaw,et al. A method for numerical integration on an automatic computer , 1960 .
[13] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[14] Johannes Gehrke,et al. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.
[15] Jeremy Nixon,et al. Measuring Calibration in Deep Learning , 2019, CVPR Workshops.
[16] Milos Hauskrecht,et al. Obtaining Well Calibrated Probabilities Using Bayesian Binning , 2015, AAAI.
[17] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[18] G. Brier. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .
[19] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[20] Christian Gagné,et al. Unsupervised Temperature Scaling: Post-Processing Unsupervised Calibration of Deep Models Decisions , 2019, ArXiv.
[21] Peter A. Flach,et al. Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration , 2017 .
[22] Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
[23] Antoine Wehenkel,et al. Unconstrained Monotonic Neural Networks , 2019, BNAIC/BENELEARN.
[24] Peter A. Flach,et al. Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration , 2019, NeurIPS.
[25] Tomas Pfister,et al. Distance-Based Learning from Errors for Confidence Calibration , 2020, ICLR.
[26] Bianca Zadrozny,et al. Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.
[27] Andrew Gordon Wilson,et al. A Simple Baseline for Bayesian Uncertainty in Deep Learning , 2019, NeurIPS.
[28] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[29] F. Facchinei,et al. Finite-Dimensional Variational Inequalities and Complementarity Problems , 2003 .
[30] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[31] Yaser Sheikh,et al. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[34] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[35] Frederick R. Forst,et al. On robust estimation of the location parameter , 1980 .
[36] Sebastian Nowozin,et al. Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift , 2019, NeurIPS.
[37] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Sunita Sarawagi,et al. Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings , 2018, ICML.
[39] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[40] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[41] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[42] Geoffrey E. Hinton,et al. When Does Label Smoothing Help? , 2019, NeurIPS.
[43] Gopinath Chennupati,et al. On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks , 2019, NeurIPS.