暂无分享,去创建一个
Daniele Gorla | Pablo Piantanida | Catuscia Palamidessi | Federica Granese | Marco Romanelli | C. Palamidessi | Marco Romanelli | P. Piantanida | D. Gorla | Federica Granese
[1] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[2] Stefano Ermon,et al. Reliable Confidence Estimation via Online Learning , 2016, ArXiv.
[3] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[4] Peter Harremoës,et al. Rényi Divergence and Kullback-Leibler Divergence , 2012, IEEE Transactions on Information Theory.
[5] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Ran El-Yaniv,et al. Selective Classification for Deep Neural Networks , 2017, NIPS.
[7] Graham W. Taylor,et al. Learning Confidence for Out-of-Distribution Detection in Neural Networks , 2018, ArXiv.
[8] Alexandre B. Tsybakov,et al. Introduction to Nonparametric Estimation , 2008, Springer series in statistics.
[9] Xia Zhu,et al. Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers , 2018, ECCV.
[10] Jianmo Ni,et al. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects , 2019, EMNLP.
[11] Yixuan Li,et al. Robust Out-of-distribution Detection in Neural Networks , 2020, ArXiv.
[12] Lysandre Debut,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[13] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[14] Weitang Liu,et al. Energy-based Out-of-distribution Detection , 2020, NeurIPS.
[15] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[16] Rauf Izmailov,et al. Complete statistical theory of learning: learning using statistical invariants , 2020, COPA.
[17] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[18] Venkatesh Saligrama,et al. Selective Classification via One-Sided Prediction , 2021, AISTATS.
[19] Percy Liang,et al. Calibrated Structured Prediction , 2015, NIPS.
[20] Matthias Hein,et al. Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Ran El-Yaniv,et al. Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers , 2018, ICLR.
[22] Ran El-Yaniv,et al. SelectiveNet: A Deep Neural Network with an Integrated Reject Option , 2019, ICML.
[23] Kibok Lee,et al. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples , 2017, ICLR.
[24] Agustinus Kristiadi,et al. Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks , 2020, ICML.
[25] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[26] Kibok Lee,et al. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , 2018, NeurIPS.
[27] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[28] Hongxia Jin,et al. Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Mert R. Sabuncu,et al. Confidence Calibration for Convolutional Neural Networks Using Structured Dropout , 2019, ArXiv.
[30] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[31] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[33] Jiayu Wu,et al. Tiny ImageNet Challenge , 2017 .
[34] Tomas Pfister,et al. Distance-Based Learning from Errors for Confidence Calibration , 2020, ICLR.