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Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning.

, , , и . NeurIPS Datasets and Benchmarks, (2021)

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Ludwig Schmidt

Ludwig Schmidt

Ludwig Schmidt

Ludwig Schmidt

Ludwig Schmidt

 

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A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k., и . CoRR, (2015)Adversarially Robust Generalization Requires More Data, , , , и . (2018)cite arxiv:1804.11285Comment: Small changes for biblatex compatibility.A fast approximation algorithm for tree-sparse recovery., , и . ISIT, стр. 1842-1846. IEEE, (2014)Do ImageNet Classifiers Generalize to ImageNet?, , , и . (2019)cite arxiv:1902.10811.Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images., , , , , , и . ICCV, стр. 2616-2627. IEEE, (2023)Better Approximations for Tree Sparsity in Nearly-Linear Time., , и . SODA, стр. 2215-2229. SIAM, (2017)CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation., , , , и . CVPR, стр. 23171-23181. IEEE, (2023)A Meta-Analysis of Overfitting in Machine Learning., , , , , , и . NeurIPS, стр. 9175-9185. (2019)Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models., , , , , , , , , и 440 other автор(ы). Trans. Mach. Learn. Res., (2023)Data Filtering Networks., , , , , и . ICLR, OpenReview.net, (2024)