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Validating Climate Models with Spherical Convolutional Wasserstein Distance Robert C. Garrett 1 Trevor Harris
Neural Information Processing Systems
The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data.
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Appendix 1 Perception Test at a glance
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Performance is evaluated by measuring top-1 accuracy.
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Muharaf: Manuscripts of Handwritten Arabic Dataset for Cursive Text Recognition
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We present the Manuscripts of Handwritten Arabic (Muharaf) dataset, which is a machine learning dataset consisting of more than 1,600 historic handwritten page images transcribed by experts in archival Arabic.
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Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
Neural Information Processing Systems
We study the task of learning SIMs in the agnostic (a.k.a.
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