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Calibrating deep neural models plays an important role in building reliable, robust AI systems in safety-critical applications.
Calibration of encoder decoder models for neural machine translation
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Predicting good probabilities with supervised learning
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
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Segmentation and recognition using structure from motion point clouds
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Construction of a 3d probabilistic atlas of human cortical structures
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Improved trainable calibration method for neural networks on medical imaging classification
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The pascal visual object classes (voc) challenge
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An analysis of single-layer networks in unsupervised feature learning
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Learning word vectors for sentiment analysis
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Speech recognition with deep recurrent neural networks
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3d object representations for fine-grained categorization
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Recursive deep models for semantic compositionality over a sentiment treebank
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Learning fair representations
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Improving credit card fraud detection with calibrated probabilities
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Network in network
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Microsoft coco: Common objects in context
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Weight uncertainty in neural network
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Electricity futures price models: Calibration and forecasting
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Variational dropout and the local reparameterization trick
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Tiny imagenet visual recognition challenge.
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015
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Obtaining well calibrated probabilities using bayesian binning
Naeini, M. P., Cooper, G. F., & Hauskrecht, M. (2015) · 2015
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End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016
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Delaney, E., Greene, D., & Keane, M. T. (2021) · 2021
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Local temperature scaling for probability calibration
Ding, Z., Han, X., Liu, P., & Niethammer, M. (2021) · 2021
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A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2021) · 2021
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A survey on uncertainty estimation in deep learning classification systems from a bayesian perspective
Mena, J., Pujol, O., & Vitria, J. (2021) · 2021
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Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016) · 2016
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Muehleisen, R. T., & Bergerson, J. (2016)
2016
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Zagoruyko, S., & Komodakis, N. (2016) · 2016
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Bayesian recurrent neural networks
Fortunato, M., Blundell, C., & Vinyals, O. (2017) · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., & Poole, B. (2017) · 2017
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Mukhoti, J., van Amersfoort, J., Torr, P. H., & Gal, Y. (2021) · 2021
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Uncertainty evaluation of object detection algorithms for autonomous vehicles
Peng, L., Wang, H., & Li, J. (2021) · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · 2021
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Uncertainty quantification and deep ensembles
Rahaman, R., et al. (2021) · 2021
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Mlp-mixer: An all-mlp architecture for vision
Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al. (2021) · 2021
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Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · 2021
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Deep calibration of financial models: turning theory into practice
Büchel, P., Kratochwil, M., Nagl, M., & Rösch, D. (2022) · 2022
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A stitch in time saves nine: A train-time regularizing loss for improved neural network calibration
Hebbalaguppe, R., Prakash, J., Madan, N., & Arora, C. (2022) · 2022
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The devil is in the margin: Margin-based label smoothing for network calibration
Liu, B., Ben Ayed, I., Galdran, A., & Dolz, J. (2022) · 2022
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On the calibration of pre-trained language models using mixup guided by area under the margin and saliency
Park, S. Y., & Caragea, C. (2022) · 2022
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Transformer uncertainty estimation with hierarchical stochastic attention
Pei, J., Wang, C., & Szarvas, G. (2022) · 2022
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Mitigating bias in calibration error estimation
Roelofs, R., Cain, N., Shlens, J., & Mozer, M. C. (2022) · 2022
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Calibrated learning to defer with one-vs-all classifiers
Verma, R., & Nalisnick, E. (2022) · 2022
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Calibrating imbalanced classifiers with focal loss: An empirical study
Wang, C., Balazs, J., Szarvas, G., Ernst, P., Poddar, L., & Danchenko, P. (2022) · 2022
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When and how mixup improves calibration
Zhang, L., Deng, Z., Kawaguchi, K., & Zou, J. (2022) · 2022
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Calibrating sequence likelihood improves conditional language generation
Zhao, Y., Khalman, M., Joshi, R., Narayan, S., Saleh, M., & Liu, P. J. (2022) · 2022
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Meta-calibration: Learning of model calibration using differentiable expected calibration error
Bohdal, O., Yang, Y., & Hospedales, T. (2023) · 2023
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Assessing and enforcing fairness in the ai lifecycle.
Calegari, R., Castañé, G. G., Milano, M., & O’Sullivan, B. (2023) · 2023
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Mitigating label biases for in-context learning
Fei, Y., Hou, Y., Chen, Z., & Bosselut, A. (2023) · 2023
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Uncertainty-aware credit card fraud detection using deep learning
Habibpour, M., Gharoun, H., Mehdipour, M., Tajally, A., Asgharnezhad, H., Shamsi, A., Khosravi, A., & Nahavandi, S. (2023) · 2023
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Prototypical calibration for few-shot learning of language models
Han, Z., Hao, Y., Dong, L., Sun, Y., & Wei, F. (2023) · 2023
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Generative calibration for in-context learning
Jiang, Z., Zhang, Y., Liu, C., Zhao, J., & Liu, K. (2023) · 2023
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Enabling calibration in the zero-shot inference of large vision-language models
LeVine, W., Pikus, B., Raj, P., & Gil, F. A. (2023) · 2023
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Calibrating deep neural networks using explicit regularisation and dynamic data pruning
Patra, R., Hebbalaguppe, R., Dash, T., Shroff, G., & Vig, L. (2023) · 2023
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Towards unraveling calibration biases in medical image analysis
Ricci Lara, M. A., Mosquera, C., Ferrante, E., & Echeveste, R. (2023) · 2023
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Classifier calibration: a survey on how to assess and improve predicted class probabilities
Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023) · 2023
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Beyond in-domain scenarios: robust density-aware calibration
Tomani, C., Waseda, F. K., Shen, Y., & Cremers, D. (2023) · 2023
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Meta-calibration regularized neural networks
Wang, C., & Golebiowski, J. (2023) · 2023
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Slic-hf: Sequence likelihood calibration with human feedback
Zhao, Y., Joshi, R., Liu, T., Khalman, M., Saleh, M., & Liu, P. J. (2023) · 2023
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On large language models’ selection bias in multi-choice questions
Zheng, C., Zhou, H., Meng, F., Zhou, J., & Huang, M. (2023) · 2023
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Batch calibration: Rethinking calibration for in-context learning and prompt engineering
Zhou, H., Wan, X., Proleev, L., Mincu, D., Chen, J., Heller, K., & Roy, S. (2023) · 2023
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Adaptive calibrator ensemble: Navigating test set difficulty in out-of-distribution scenarios
Zou, Y., Deng, W., & Zheng, L. (2023) · 2023
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On the limitations of temperature scaling for distributions with overlaps
Chidambaram, M., & Ge, R. (2024) · 2024
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Confidence calibration of classifiers with many classes
Le Coz, A., Herbin, S., & Adjed, F. (2024) · 2024
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Confidence calibration of a medical imaging classification system that is robust to label noise
Penso, C., Frenkel, L., & Goldberger, J. (2024) · 2024
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Thermometer: towards universal calibration for large language models
Shen, M., Das, S., Greenewald, K., Sattigeri, P., Wornell, G., & Ghosh, S. (2024) · 2024
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Atomic calibration of llms in long-form generations
Zhang, C., Yang, R., Zhang, Z., Huang, X., Yang, S., Yu, D., & Collier, N. (2024) · 2024
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Uncertainty-aware online extrinsic calibration: A conformal prediction approach
Cocheteux, M., Moreau, J., & Davoine, F. (2025) · 2025
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Uncertainty weighted gradients for model calibration
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