A Survey on Learning to Reject
暂无分享,去创建一个
[1] Xu-Yao Zhang,et al. Rethinking Confidence Calibration for Failure Prediction , 2023, ECCV.
[2] Lior Wolf,et al. DeepFake Detection Based on Discrepancies Between Faces and Their Context , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] R. Manmatha,et al. On Calibration of Scene-Text Recognition Models , 2020, ECCV Workshops.
[4] Fei Yin,et al. Convolutional Prototype Network for Open Set Recognition , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Nicolas Thome,et al. Confidence Estimation via Auxiliary Models , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Yong Jiang,et al. Backdoor Learning: A Survey , 2020, IEEE transactions on neural networks and learning systems.
[7] Yisroel Mirsky,et al. The Creation and Detection of Deepfakes , 2020, ACM Comput. Surv..
[8] Saeid Nahavandi,et al. Deep learning for deepfakes creation and detection: A survey , 2019, Comput. Vis. Image Underst..
[9] Songcan Chen,et al. Collective Decision for Open Set Recognition , 2018, IEEE Transactions on Knowledge and Data Engineering.
[10] Dawn Song,et al. Scaling Out-of-Distribution Detection for Real-World Settings , 2022, ICML.
[11] A. Shabtai,et al. FOOD: Fast Out-Of-Distribution Detector , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).
[12] Emiliano De Cristofaro. A Critical Overview of Privacy in Machine Learning , 2021, IEEE Security & Privacy.
[13] Ran He,et al. Inconsistency-Aware Wavelet Dual-Branch Network for Face Forgery Detection , 2021, IEEE Transactions on Biometrics, Behavior, and Identity Science.
[14] B. Sengupta,et al. On Deep Neural Network Calibration by Regularization and its Impact on Refinement , 2021, 2106.09385.
[15] Simon S. Woo,et al. FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[16] Nenghai Yu,et al. Initiative Defense against Facial Manipulation , 2021, AAAI.
[17] Weihong Deng,et al. Representative Forgery Mining for Fake Face Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Jun Zhu,et al. LiBRe: A Practical Bayesian Approach to Adversarial Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Prateek Mittal,et al. SSD: A Unified Framework for Self-Supervised Outlier Detection , 2021, ICLR.
[20] J. Keuper,et al. SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).
[21] Nenghai Yu,et al. Multi-attentional Deepfake Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Jing Dong,et al. Exploring Adversarial Fake Images on Face Manifold , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Felippe Schmoeller Roza,et al. From Black-box to White-box: Examining Confidence Calibration under different Conditions , 2021, SafeAI@AAAI.
[24] Yuanjun Xiong,et al. Learning Self-Consistency for Deepfake Detection , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[25] Bin Dong,et al. Feature Space Singularity for Out-of-Distribution Detection , 2020, SafeAI@AAAI.
[26] Zhibin Liao,et al. Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection , 2020, IEEE Transactions on Medical Imaging.
[27] Cristian Canton-Ferrer,et al. Adversarial Threats to DeepFake Detection: A Practical Perspective , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[28] Theodoros Tsiligkaridis,et al. Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks , 2020, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[29] Hao Lu,et al. SESV: Accurate Medical Image Segmentation by Predicting and Correcting Errors , 2020, IEEE Transactions on Medical Imaging.
[30] Sahar Abdelnabi,et al. Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data , 2020, IEEE International Conference on Computer Vision.
[31] Yixin Chen,et al. Watermarking Deep Neural Networks in Image Processing , 2020, IEEE Transactions on Neural Networks and Learning Systems.
[32] F. Koushanfar,et al. Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples , 2020, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[33] Hang Su,et al. OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples , 2020, IEEE Transactions on Visualization and Computer Graphics.
[34] Yingli Tian,et al. Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Songcan Chen,et al. Recent Advances in Open Set Recognition: A Survey , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] Xinyi Zhou,et al. A Survey of Fake News , 2020, ACM Comput. Surv..
[37] Alan Wee-Chung Liew,et al. Preventing DeepFake Attacks on Speaker Authentication by Dynamic Lip Movement Analysis , 2021, IEEE Transactions on Information Forensics and Security.
[38] Yixuan Li,et al. Energy-based Out-of-distribution Detection , 2020, NeurIPS.
[39] Myunggwon Hwang,et al. Out-of-Distribution Detection Based on Distance Metric Learning , 2020, SMA.
[40] Laurie A. Harris,et al. Deep Fakes and National Security , 2020 .
[41] Dongha Lee,et al. Multi-Class Data Description for Out-of-distribution Detection , 2020, KDD.
[42] Iacopo Masi,et al. Two-branch Recurrent Network for Isolating Deepfakes in Videos , 2020, ECCV.
[43] B. Amini,et al. Measuring and Teaching Confidence Calibration Among Radiologists: A Multi-Institution Study. , 2020, Journal of the American College of Radiology : JACR.
[44] Jinwoo Shin,et al. CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances , 2020, NeurIPS.
[45] Chandramouli Shama Sastry,et al. Detecting Out-of-Distribution Examples with Gram Matrices , 2020, ICML.
[46] Sangheum Hwang,et al. Confidence-Aware Learning for Deep Neural Networks , 2020, ICML.
[47] Gregory Shakhnarovich,et al. Classification Confidence Estimation with Test-Time Data-Augmentation , 2020, ArXiv.
[48] S. Kambhampati,et al. Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification , 2020, ArXiv.
[49] Xiaoning Qian,et al. NADS: Neural Architecture Distribution Search for Uncertainty Awareness , 2020, ICML.
[50] Vishal M. Patel,et al. Generative-Discriminative Feature Representations for Open-Set Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Simon S. Woo,et al. OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[52] Sunny Raj,et al. Detecting Deepfake Videos using Attribution-Based Confidence Metric , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[53] Garrett T. Kenyon,et al. Modeling Biological Immunity to Adversarial Examples , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Nasser M. Nasrabadi,et al. Exploiting Joint Robustness to Adversarial Perturbations , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Yi Li,et al. Background Data Resampling for Outlier-Aware Classification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Fahad Shahbaz Khan,et al. A Self-supervised Approach for Adversarial Robustness , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Ching Y. Suen,et al. Towards Robust Pattern Recognition: A Review , 2020, Proceedings of the IEEE.
[58] Rushil Anirudh,et al. Designing accurate emulators for scientific processes using calibration-driven deep models , 2020, Nature Communications.
[59] Yedid Hoshen,et al. Classification-Based Anomaly Detection for General Data , 2020, ICLR.
[60] Zhihai He,et al. Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[61] Oliver Giudice,et al. DeepFake Detection by Analyzing Convolutional Traces , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[62] Graham Neubig,et al. Weight Poisoning Attacks on Pretrained Models , 2020, ACL.
[63] Udi Weinsberg,et al. Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks , 2020, WWW.
[64] Hong Yu,et al. Calibrating Structured Output Predictors for Natural Language Processing , 2020, ACL.
[65] Hany Farid,et al. Evading Deepfake-Image Detectors with White- and Black-Box Attacks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[66] Yu Cheng,et al. Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Ang Li,et al. Hybrid Models for Open Set Recognition , 2020, ECCV.
[68] Xin Sun,et al. Conditional Gaussian Distribution Learning for Open Set Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[69] Dorothea Kolossa,et al. Leveraging Frequency Analysis for Deep Fake Image Recognition , 2020, ICML.
[70] Bhavya Kailkhura,et al. Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning , 2020, ICML.
[71] Byron Boots,et al. Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks , 2020, NeurIPS.
[72] Dinesh Manocha,et al. Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues , 2020, ACM Multimedia.
[73] Michelle Karg,et al. Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[74] Gustavo Carneiro,et al. Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy , 2020, Medical Image Anal..
[75] Harsha Vardhan Simhadri,et al. DROCC: Deep Robust One-Class Classification , 2020, ICML.
[76] Z. Kira,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).
[77] Philipp Hennig,et al. Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks , 2020, ICML.
[78] Philip H. S. Torr,et al. Calibrating Deep Neural Networks using Focal Loss , 2020, NeurIPS.
[79] Ben Y. Zhao,et al. Fawkes: Protecting Privacy against Unauthorized Deep Learning Models , 2020, USENIX Security Symposium.
[80] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[81] Chen Chen,et al. An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models , 2020, 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[82] Philip H. S. Torr,et al. Global Texture Enhancement for Fake Face Detection in the Wild , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[83] Luisa Verdoliva,et al. Media Forensics and DeepFakes: An Overview , 2020, IEEE Journal of Selected Topics in Signal Processing.
[84] Tamara Miner Haygood,et al. Confidence Calibration: An Introduction With Application to Quality Improvement. , 2020, Journal of the American College of Radiology : JACR.
[85] A. Morales,et al. DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection , 2020, Inf. Fusion.
[86] Fang Wen,et al. Face X-Ray for More General Face Forgery Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[87] Honglak Lee,et al. Efficient Adversarial Training With Transferable Adversarial Examples , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[88] Derek Hoiem,et al. Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[89] J. Gilmer,et al. AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty , 2019, ICLR.
[90] Sercan Ö. Arik,et al. Distance-Based Learning from Errors for Confidence Calibration , 2019, ICLR.
[91] Sae-Young Chung,et al. Novelty Detection Via Blurring , 2019, ICLR.
[92] Ziwei Liu,et al. When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[93] Deepta Rajan,et al. Learn-By-Calibrating: Using Calibration As A Training Objective , 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[94] V. Gómez,et al. Input complexity and out-of-distribution detection with likelihood-based generative models , 2019, ICLR.
[95] Jiliang Tang,et al. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review , 2019, International Journal of Automation and Computing.
[96] Xia Hu,et al. Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder , 2019, CIKM.
[97] Felix Juefei-Xu,et al. FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces , 2019, IJCAI.
[98] Minlie Huang,et al. Out-of-Domain Detection for Natural Language Understanding in Dialog Systems , 2019, IEEE/ACM Transactions on Audio, Speech, and Language Processing.
[99] D. Scaramuzza,et al. A General Framework for Uncertainty Estimation in Deep Learning , 2019, IEEE Robotics and Automation Letters.
[100] Rudolph Triebel,et al. Non-Parametric Calibration for Classification , 2019, AISTATS.
[101] Min Lu,et al. Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions , 2019, WWW.
[102] Tiago M. Fernández-Caramés,et al. Fake News, Disinformation, and Deepfakes: Leveraging Distributed Ledger Technologies and Blockchain to Combat Digital Deception and Counterfeit Reality , 2019, IT Professional.
[103] Jinjun Xiong,et al. Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[104] Ilke Demir,et al. FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals , 2019, IEEE transactions on pattern analysis and machine intelligence.
[105] Mohamed H. Zaki,et al. Uncertainty in Neural Networks: Approximately Bayesian Ensembling , 2018, AISTATS.
[106] Jiliang Zhang,et al. Adversarial Examples: Opportunities and Challenges , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[107] Kiyoharu Aizawa,et al. Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[108] Giacomo Spigler,et al. Denoising Autoencoders for Overgeneralization in Neural Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[109] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[110] Masanori Suganuma,et al. Hyperparameter-Free Out-of-Distribution Detection Using Cosine Similarity , 2020, ACCV.
[111] Blaise Hanczar,et al. Performance visualization spaces for classification with rejection option , 2019, Pattern Recognit..
[112] Ousmane Amadou Dia,et al. Adversarial Examples in Modern Machine Learning: A Review , 2019, ArXiv.
[113] Ronald Kemker,et al. Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets? , 2019, ArXiv.
[114] Rick Salay,et al. Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output , 2019, ArXiv.
[115] Christoph H. Lampert,et al. KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications , 2019, International Journal of Computer Vision.
[116] Rick Salay,et al. Out-of-distribution Detection in Classifiers via Generation , 2019, ArXiv.
[117] Damian Borth,et al. Adversarial Learning of Deepfakes in Accounting , 2019, ArXiv.
[118] Seunghoon Hong,et al. Adversarial Defense via Learning to Generate Diverse Attacks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[119] Matthieu Cord,et al. Addressing Failure Prediction by Learning Model Confidence , 2019, NeurIPS.
[120] Qingjie Zhao,et al. Detecting adversarial examples via prediction difference for deep neural networks , 2019, Inf. Sci..
[121] Percy Liang,et al. Verified Uncertainty Calibration , 2019, NeurIPS.
[122] Peter A. Flach,et al. Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration , 2019, NeurIPS.
[123] Younghak Shin,et al. Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[124] Kiyoharu Aizawa,et al. Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[125] Chuangyin Dang,et al. Calibrating Classification Probabilities with Shape-Restricted Polynomial Regression , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[126] Dawn Song,et al. Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty , 2019, NeurIPS.
[127] Baoyuan Wu,et al. Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations , 2019, ArXiv.
[128] Jasper Snoek,et al. Likelihood Ratios for Out-of-Distribution Detection , 2019, NeurIPS.
[129] Geoffrey E. Hinton,et al. When Does Label Smoothing Help? , 2019, NeurIPS.
[130] Edward Raff,et al. Barrage of Random Transforms for Adversarially Robust Defense , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[131] Sergio Escalera,et al. What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks? , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[132] Song Han,et al. Deep Leakage from Gradients , 2019, NeurIPS.
[133] Gopinath Chennupati,et al. On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks , 2019, NeurIPS.
[134] Vojtech Franc,et al. On discriminative learning of prediction uncertainty , 2019, ICML.
[135] Dina Katabi,et al. ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation , 2019, ICML.
[136] Marcin Detyniecki,et al. Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection , 2019, ArXiv.
[137] 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).
[138] Azadeh Sadat Mozafari,et al. Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks , 2019 .
[139] Ling Shao,et al. Out-Of-Distribution Detection for Generalized Zero-Shot Action Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[140] Minyi Guo,et al. Adversarial Defense Through Network Profiling Based Path Extraction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[141] Jeremy Nixon,et al. Measuring Calibration in Deep Learning , 2019, CVPR Workshops.
[142] Vishal M. Patel,et al. C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[143] Ling Shao,et al. Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[144] Ramesh Nallapati,et al. OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[145] Khaled Salah,et al. Combating Deepfake Videos Using Blockchain and Smart Contracts , 2019, IEEE Access.
[146] Vishal M. Patel,et al. Deep Transfer Learning for Multiple Class Novelty Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[147] Jinjun Xiong,et al. Evaluation of Neural Network Uncertainty Estimation with Application to Resource-Constrained Platforms , 2019, ArXiv.
[148] Abhimanyu Dubey,et al. Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[149] Sunita Sarawagi,et al. Calibration of Encoder Decoder Models for Neural Machine Translation , 2019, ArXiv.
[150] Andrew Gordon Wilson,et al. A Simple Baseline for Bayesian Uncertainty in Deep Learning , 2019, NeurIPS.
[151] Masashi Sugiyama,et al. On the Calibration of Multiclass Classification with Rejection , 2019, NeurIPS.
[152] Kimin Lee,et al. Using Pre-Training Can Improve Model Robustness and Uncertainty , 2019, ICML.
[153] Ran El-Yaniv,et al. SelectiveNet: A Deep Neural Network with an Integrated Reject Option , 2019, ICML.
[154] Suchi Saria,et al. Can You Trust This Prediction? Auditing Pointwise Reliability After Learning , 2019, AISTATS.
[155] 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).
[156] Takeshi Naemura,et al. Classification-Reconstruction Learning for Open-Set Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[157] Dustin Tran,et al. Bayesian Layers: A Module for Neural Network Uncertainty , 2018, NeurIPS.
[158] Alan L. Yuille,et al. Feature Denoising for Improving Adversarial Robustness , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[159] Tribhuvanesh Orekondy,et al. Knockoff Nets: Stealing Functionality of Black-Box Models , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[160] Xiaochun Cao,et al. ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[161] Deliang Fan,et al. Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[162] Jonathan P. How,et al. Safe Reinforcement Learning With Model Uncertainty Estimates , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[163] Thomas G. Dietterich,et al. Deep Anomaly Detection with Outlier Exposure , 2018, ICLR.
[164] Yee Whye Teh,et al. Do Deep Generative Models Know What They Don't Know? , 2018, ICLR.
[165] 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).
[166] James J. Little,et al. A Less Biased Evaluation of Out-of-distribution Sample Detectors , 2018, BMVC.
[167] Frank Hutter,et al. Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..
[168] Dongdong Hou,et al. Detection Based Defense Against Adversarial Examples From the Steganalysis Point of View , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[169] Ran El-Yaniv,et al. Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers , 2018, ICLR.
[170] Karthikeyan Shanmugam,et al. Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes , 2018, AISTATS.
[171] Samuel Marchal,et al. PRADA: Protecting Against DNN Model Stealing Attacks , 2018, 2019 IEEE European Symposium on Security and Privacy (EuroS&P).
[172] Tao Liu,et al. Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[173] Stefan Wermter,et al. Continual Lifelong Learning with Neural Networks: A Review , 2018, Neural Networks.
[174] Kouichi Sakurai,et al. One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.
[175] C. L. Zitnick,et al. What does it mean to learn in deep networks ? And , how does one detect adversarial attacks ? , 2019 .
[176] Hao Li,et al. Protecting World Leaders Against Deep Fakes , 2019, CVPR Workshops.
[177] Rick Salay,et al. Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance , 2018, ArXiv.
[178] Kumar Sricharan,et al. Building robust classifiers through generation of confident out of distribution examples , 2018, ArXiv.
[179] Rick Salay,et al. Calibrating Uncertainties in Object Localization Task , 2018, ArXiv.
[180] Terrance E. Boult,et al. Reducing Network Agnostophobia , 2018, NeurIPS.
[181] Pramod K. Varshney,et al. Copula Based Classifier Fusion Under Statistical Dependence , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[182] Christian Gagn'e,et al. Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks , 2018, 1810.11586.
[183] M. de Rijke,et al. Calibration: A Simple Way to Improve Click Models , 2018, CIKM.
[184] Andrew Zisserman,et al. Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection , 2018 .
[185] Alexander A. Alemi,et al. WAIC, but Why? Generative Ensembles for Robust Anomaly Detection , 2018 .
[186] Weng-Keen Wong,et al. Open Set Learning with Counterfactual Images , 2018, ECCV.
[187] Xia Zhu,et al. Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers , 2018, ECCV.
[188] Joseph Keshet,et al. Out-of-Distribution Detection using Multiple Semantic Label Representations , 2018, NeurIPS.
[189] Yaser Sheikh,et al. Recycle-GAN: Unsupervised Video Retargeting , 2018, ECCV.
[190] Joost van de Weijer,et al. Metric Learning for Novelty and Anomaly Detection , 2018, BMVC.
[191] Hongxia Yang,et al. Adversarial Detection with Model Interpretation , 2018, KDD.
[192] Kibok Lee,et al. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , 2018, NeurIPS.
[193] Stanislav Pidhorskyi,et al. Generative Probabilistic Novelty Detection with Adversarial Autoencoders , 2018, NeurIPS.
[194] Alexander Binder,et al. Deep One-Class Classification , 2018, ICML.
[195] Sunita Sarawagi,et al. Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings , 2018, ICML.
[196] Xiaolin Hu,et al. Interpret Neural Networks by Identifying Critical Data Routing Paths , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[197] Luc Van Gool,et al. Generative Adversarial Style Transfer Networks for Face Aging , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[198] Patrick Nguyen,et al. Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis , 2018, NeurIPS.
[199] Atul Prakash,et al. Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[200] Luc Van Gool,et al. Failure Prediction for Autonomous Driving , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).
[201] Ran El-Yaniv,et al. Deep Anomaly Detection Using Geometric Transformations , 2018, NeurIPS.
[202] Maya R. Gupta,et al. To Trust Or Not To Trust A Classifier , 2018, NeurIPS.
[203] Ian S. Fischer,et al. Learning to Attack: Adversarial Transformation Networks , 2018, AAAI.
[204] Gregory Shakhnarovich,et al. Confidence from Invariance to Image Transformations , 2018, ArXiv.
[205] Tudor Dumitras,et al. Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks , 2018, NeurIPS.
[206] Patrick D. McDaniel,et al. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning , 2018, ArXiv.
[207] Mahmood Fathy,et al. Adversarially Learned One-Class Classifier for Novelty Detection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[208] Jasper Snoek,et al. Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling , 2018, ICLR.
[209] Kamyar Azizzadenesheli,et al. Stochastic Activation Pruning for Robust Adversarial Defense , 2018, ICLR.
[210] Bo Zong,et al. Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.
[211] Rama Chellappa,et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models , 2018, ICLR.
[212] Graham W. Taylor,et al. Learning Confidence for Out-of-Distribution Detection in Neural Networks , 2018, ArXiv.
[213] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[214] Matthias Bethge,et al. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models , 2017, ICLR.
[215] Xiaolin Hu,et al. Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[216] Kibok Lee,et al. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples , 2017, ICLR.
[217] Alan L. Yuille,et al. Mitigating adversarial effects through randomization , 2017, ICLR.
[218] Yang Song,et al. PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , 2017, ICLR.
[219] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[220] R. Srikant,et al. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks , 2017, ICLR.
[221] Julian Togelius,et al. DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution* , 2017, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).
[222] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[223] Yanjun Qi,et al. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.
[224] Terrance E. Boult,et al. The Extreme Value Machine , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[225] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[226] Pieter Abbeel,et al. Safer Classification by Synthesis , 2017, ArXiv.
[227] Geoff Holmes,et al. Probability Calibration Trees , 2017, ACML.
[228] Yong Yu,et al. Face Transfer with Generative Adversarial Network , 2017, ArXiv.
[229] Daphna Weinshall,et al. Distance-based Confidence Score for Neural Network Classifiers , 2017, ArXiv.
[230] Lei Shu,et al. DOC: Deep Open Classification of Text Documents , 2017, EMNLP.
[231] Jinfeng Yi,et al. ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models , 2017, AISec@CCS.
[232] Rahil Garnavi,et al. Generative OpenMax for Multi-Class Open Set Classification , 2017, BMVC.
[233] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[234] Hao Chen,et al. MagNet: A Two-Pronged Defense against Adversarial Examples , 2017, CCS.
[235] Yang Yu,et al. Open Category Classification by Adversarial Sample Generation , 2017, IJCAI.
[236] Vishal M. Patel,et al. Sparse Representation-Based Open Set Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[237] Ran El-Yaniv,et al. Selective Classification for Deep Neural Networks , 2017, NIPS.
[238] Zhitao Gong,et al. Adversarial and Clean Data Are Not Twins , 2017, aiDM@SIGMOD.
[239] Peter A. Flach,et al. Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers , 2017, AISTATS.
[240] David A. Forsyth,et al. SafetyNet: Detecting and Rejecting Adversarial Examples Robustly , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[241] Gang Hua,et al. CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[242] Radha Poovendran,et al. Blocking Transferability of Adversarial Examples in Black-Box Learning Systems , 2017, ArXiv.
[243] Jan Hendrik Metzen,et al. On Detecting Adversarial Perturbations , 2017, ICLR.
[244] Geoffrey E. Hinton,et al. Regularizing Neural Networks by Penalizing Confident Output Distributions , 2017, ICLR.
[245] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[246] Christoph H. Lampert,et al. iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[247] Siegfried Wahl,et al. Leveraging uncertainty information from deep neural networks for disease detection , 2016, Scientific Reports.
[248] Seyed-Mohsen Moosavi-Dezfooli,et al. Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[249] Kevin Gimpel,et al. A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.
[250] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[251] Kevin Gimpel,et al. Early Methods for Detecting Adversarial Images , 2016, ICLR.
[252] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[253] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[254] Jelena Kovacevic,et al. Performance measures for classification systems with rejection , 2015, Pattern Recognit..
[255] Martin Wistuba,et al. Harnessing Model Uncertainty for Detecting Adversarial Examples , 2017 .
[256] Ricardo da Silva Torres,et al. Nearest neighbors distance ratio open-set classifier , 2016, Machine Learning.
[257] Mehryar Mohri,et al. Learning with Rejection , 2016, ALT.
[258] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[259] Mahdi Pakdaman Naeini,et al. Binary Classifier Calibration Using an Ensemble of Linear Trend Estimation , 2016, SDM.
[260] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[261] Terrance E. Boult,et al. Towards Open Set Deep Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[262] Mahdi Pakdaman Naeini,et al. Binary Classifier Calibration Using an Ensemble of Near Isotonic Regression Models , 2015, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[263] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[264] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[265] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[266] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[267] Gang Hua,et al. Learning Discriminative Reconstructions for Unsupervised Outlier Removal , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[268] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[269] Takaya Saito,et al. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.
[270] Milos Hauskrecht,et al. Obtaining Well Calibrated Probabilities Using Bayesian Binning , 2015, AAAI.
[271] Xiaojin Zhu,et al. Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners , 2015, AAAI.
[272] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[273] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[274] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[275] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[276] David A. Clifton,et al. A review of novelty detection , 2014, Signal Process..
[277] Ben Goertzel,et al. Artificial General Intelligence: Concept, State of the Art, and Future Prospects , 2009, J. Artif. Gen. Intell..
[278] Terrance E. Boult,et al. Probability Models for Open Set Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[279] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[280] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[281] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[282] Leon Wenliang Zhong,et al. Accurate Probability Calibration for Multiple Classifiers , 2013, IJCAI.
[283] Anderson Rocha,et al. Toward Open Set Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[284] Allyson F. Hadwin,et al. Calibration in goal setting: Examining the nature of judgments of confidence , 2013 .
[285] John Hattie,et al. Calibration and confidence: Where to next? , 2013 .
[286] Xiaoqian Jiang,et al. Predicting accurate probabilities with a ranking loss , 2012, ICML.
[287] Rama Chellappa,et al. Dictionary-Based Face Recognition Under Variable Lighting and Pose , 2012, IEEE Transactions on Information Forensics and Security.
[288] Jihoon Kim,et al. Calibrating predictive model estimates to support personalized medicine , 2011, J. Am. Medical Informatics Assoc..
[289] L. Deng,et al. Calibration of Confidence Measures in Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.
[290] Anderson Rocha,et al. Meta-Recognition: The Theory and Practice of Recognition Score Analysis , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[291] Chandan Srivastava,et al. Support Vector Data Description , 2011 .
[292] Guillermo Sapiro,et al. Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.
[293] Ran El-Yaniv,et al. On the Foundations of Noise-free Selective Classification , 2010, J. Mach. Learn. Res..
[294] Pierre Beauseroy,et al. Optimal Decision Rule with Class-Selective Rejection and Performance Constraints , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[295] Allen Y. Yang,et al. Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[296] Peter L. Bartlett,et al. Classification with a Reject Option using a Hinge Loss , 2008, J. Mach. Learn. Res..
[297] William Stafford Noble,et al. Support vector machine , 2013 .
[298] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[299] Pavel Pudil,et al. Introduction to Statistical Pattern Recognition , 2006 .
[300] Kilian Q. Weinberger,et al. Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.
[301] Rich Caruana,et al. Predicting good probabilities with supervised learning , 2005, ICML.
[302] Cheng-Lin Liu,et al. Classifier combination based on confidence transformation , 2005, Pattern Recognit..
[303] Victoria J. Hodge,et al. A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.
[304] Alex Kulesza,et al. Confidence Estimation for Machine Translation , 2004, COLING.
[305] Yiming Yang,et al. Probabilistic score estimation with piecewise logistic regression , 2004, ICML.
[306] Hiroshi Sako,et al. Confidence Transformation for Combining Classifiers , 2004, Pattern Analysis and Applications.
[307] Chih-Jen Lin,et al. Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..
[308] Alfons Juan-Císcar,et al. Improving utterance verification using a smoothed naive Bayes model , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[309] Eric P. Smith,et al. An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.
[310] Yves Normandin,et al. Robust semantic confidence scoring , 2002, INTERSPEECH.
[311] Bianca Zadrozny,et al. Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.
[312] Rong Zhang,et al. Word level confidence annotation using combinations of features , 2001, INTERSPEECH.
[313] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[314] Bianca Zadrozny,et al. Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.
[315] Mario Vento,et al. To reject or not to reject: that is the question-an answer in case of neural classifiers , 2000, IEEE Trans. Syst. Man Cybern. Part C.
[316] Bernhard Schölkopf,et al. Support Vector Method for Novelty Detection , 1999, NIPS.
[317] Vladimir Vapnik,et al. An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.
[318] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[319] Yoram Baram,et al. Partial Classification: The Benefit of Deferred Decision , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[320] Ron Kohavi,et al. The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.
[321] Matteo Golfarelli,et al. On the Error-Reject Trade-Off in Biometric Verification Systems , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[322] Thien M. Ha,et al. The Optimum Class-Selective Rejection Rule , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[323] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[324] Mario Vento,et al. A method for improving classification reliability of multilayer perceptrons , 1995, IEEE Trans. Neural Networks.
[325] Christopher M. Bishop,et al. Novelty detection and neural network validation , 1994 .
[326] Pierre Courrieu,et al. Three algorithms for estimating the domain of validity of feedforward neural networks , 1994, Neural Networks.
[327] Bernard Dubuisson,et al. A statistical decision rule with incomplete knowledge about classes , 1993, Pattern Recognit..
[328] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[329] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[330] Stephen E. Fienberg,et al. The Comparison and Evaluation of Forecasters. , 1983 .
[331] Martin E. Hellman,et al. The Nearest Neighbor Classification Rule with a Reject Option , 1970, IEEE Trans. Syst. Sci. Cybern..
[332] C. K. Chow,et al. On optimum recognition error and reject tradeoff , 1970, IEEE Trans. Inf. Theory.
[333] C. K. Chow,et al. An optimum character recognition system using decision functions , 1957, IRE Trans. Electron. Comput..
[334] G. Brier. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .