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Technological and computational advances continuously drive forward the broad field of deep learning.
The comparison and evaluation of forecasters
DeGroot, M. H. and Fienberg, S. E · 1983
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Identifying mislabeled training data
Brodley, C. E. and Friedl, M. A · 1999
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
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Length-dependent prediction of protein intrinsic disorder
Peng, K., Radivojac, P., Vucetic, S., Dunker, A. K., and Obradovic, Z · 2006
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Local climate zones: Origins, development, and application to urban heat island studies
Stewart, I · 2011
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Local climate zones for urban temperature studies
Stewart, I. D. and Oke, T. R · 2012
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Classification in the presence of label noise: a survey
Frénay, B. and Verleysen, M · 2013
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Facial age estimation by learning from label distributions
Geng, X., Yin, C., and Zhou, Z.-H · 2013
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Attribute-based classification for zero-shot visual object categorization
Lampert, C. H., Nickisch, H., and Harmeling, S · 2013
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Local climate classification and dublin’s urban heat island
Alexander, P. J. and Mills, G · 2014
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Design of an urban monitoring network based on local climate zone mapping and temperature pattern modelling
Lelovics, E., Unger, J., Gál, T., and Gál, C. V · 2014
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Evaluation of the ‘local climate zone’scheme using temperature observations and model simulations
Stewart, I. D., Oke, T. R., and Krayenhoff, E. S · 2014
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Analysis of urban heat island in kochi, india, using a modified local climate zone classification
Thomas, G., Sherin, A., Ansar, S., and Zachariah, E · 2014
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Mapping local climate zones for a worldwide database of the form and function of cities
Bechtel, B., Alexander, P. J., Böhner, J., Ching, J., Conrad, O., Feddema, J., Mills, G., See, L., and Stewart, I · 2015
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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An introduction to the wudapt project
Mills, G., Ching, J., See, L., Bechtel, B., and Foley, M · 2015
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Wudapt, an efficient land use producing data tool for mesoscale models? integration of urban lcz in wrf over madrid
Brousse, O., Martilli, A., Foley, M., Mills, G., and Bechtel, B · 2016
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Contributing to wudapt: A local climate zone classification of two cities in ukraine
Danylo, O., See, L., Bechtel, B., Schepaschenko, D., and Fritz, S · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Label distribution learning
Geng, X · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Deep label distribution learning with label ambiguity
Gao, B.-B., Xing, C., Xie, C.-W., Wu, J., and Geng, X · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Deep learning is robust to massive label noise
Rolnick, D., Veit, A., Belongie, S., and Shavit, N · 2017
Human uncertainty makes classification more robust
Peterson, J. C., Battleday, R. M., Griffiths, T. L., and Russakovsky, O · 2019
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Local climate zone-based urban land cover classification from multi-seasonal sentinel-2 images with a recurrent residual network
Qiu, C., Mou, L., Schmitt, M., and Zhu, X. X · 2019
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Local climate zone ventilation and urban land surface temperatures: Towards a performance-based and wind-sensitive planning proposal in megacities
Yang, J., Jin, S., Xiao, X., Jin, C., Xia, J. C., Li, X., and Wang, S · 2019
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Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using landsat images
Yoo, C., Han, D., Im, J., and Bechtel, B · 2019
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Capturing human categorization of natural images by combining deep networks and cognitive models
Battleday, R. M., Peterson, J. C., and Griffiths, T. L · 2020
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Towards computation of urban local climate zones (lcz) from openstreetmap data
Samsonov, T. and Trigub, K · 2017
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Influence of neighbourhood information on ‘local climate zone’mapping in heterogeneous cities
Verdonck, M.-L., Okujeni, A., van der Linden, S., Demuzere, M., De Wulf, R., and Van Coillie, F · 2017
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Unsupervised sar image segmentation using ambiguity label information fusion in triplet markov fields model
Wang, F., Wu, Y., Zhang, P., Zhang, Q., and Li, M · 2017
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Wudapt: An urban weather, climate, and environmental modeling infrastructure for the anthropocene
Ching, J., Mills, G., Bechtel, B., See, L., Feddema, J., Wang, X., Ren, C., Brousse, O., Martilli, A., Neophytou, M., et al · 2018
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Training a neural network based on unreliable human annotation of medical images
Dgani, Y., Greenspan, H., and Goldberger, J · 2018
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Feature extraction and selection of sentinel-1 dual-pol data for global-scale local climate zone classification
Hu, J., Ghamisi, P., and Zhu, X. X · 2018
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A dynamic end-to-end fusion filter for local climate zone classification using sar and multi-spectrum remote sensing data
Feng, P., Lin, Y., He, G., Guan, J., Wang, J., and Shi, H · 2020
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On the fusion strategies of sentinel-1 and sentinel-2 data for local climate zone classification
Gawlikowski, J., Schmitt, M., Kruspe, A., and Zhu, X. X · 2020
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Does label smoothing mitigate label noise?
Lukasik, M., Bhojanapalli, S., Menon, A., and Kumar, S · 2020
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Model and data uncertainty for satellite time series forecasting with deep recurrent models
Russwurm, M., Ali, M., Zhu, X. X., Gal, Y., and Korner, M · 2020
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Revisiting knowledge distillation via label smoothing regularization
Yuan, L., Tay, F. E., Li, G., Wang, T., and Feng, J · 2020
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So2sat lcz42: a benchmark data set for the classification of global local climate zones [software and data sets]
Zhu, X. X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., Bagheri, H., Haberle, M., Hua, Y., Huang, R., et al · 2020
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Image classification with deep learning in the presence of noisy labels: A survey
Algan, G. and Ulusoy, I · 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
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Hüllermeier, E. and Waegeman, W · 2021
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Neighbor-based label distribution learning to model label ambiguity for aerial scene classification
Luo, J., Wang, Y., Ou, Y., He, B., and Li, B · 2021
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Delving deep into label smoothing
Zhang, C.-B., Jiang, P.-T., Hou, Q., Wei, Y., Han, Q., Li, Z., and Cheng, M.-M · 2021
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Zhou, S. K., Greenspan, H., Davatzikos, C., Duncan, J. S., Van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., and Summers, R. M · 2021
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An advanced dirichlet prior network for out-of-distribution detection in remote sensing
Gawlikowski, J., Saha, S., Kruspe, A., and Zhu, X. X · 2022
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Reliability diagrams
Hollemans, M · 2022
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The urban morphology on our planet – global perspectives from space
Zhu, X. X., Qiu, C., Hu, J., Shi, Y., Wang, Y., Schmitt, M., and Taubenböck, H · 2022
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