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The acquisition of training data is crucial for machine learning applications.
A note on analytic functions in the unit circle
Raymond EAC Paley and Antoni Zygmund · 1932
Earlier work this paper cites.
Adjustment of an inverse matrix corresponding to a change in one element of a given matrix
Jack Sherman and Winifred J Morrison · 1950
Earlier work this paper cites.
The sequential generation of d d -optimum experimental designs
Henry P Wynn · 1970
Earlier work this paper cites.
Economic welfare and the allocation of resources for invention
Kenneth Joseph Arrow · 1972
Earlier work this paper cites.
Detection of influential observation in linear regression
R Dennis Cook · 1977
Earlier work this paper cites.
Optimal design measures with singular information matrices
SD Silvey · 1978
Earlier work this paper cites.
Rounding of polytopes in the real number model of computation
Leonid G Khachiyan · 1996
Earlier work this paper cites.
Elements of Information Theory
Thomas M Cover · 1999
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
MIMIC-III, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
Earlier work this paper cites.
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Finetuned language models are zero-shot learners
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Validation free and replication robust volume-based data valuation
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The rsna pediatric bone age machine learning challenge
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Analysis of the frank–wolfe method for convex composite optimization involving a logarithmically-homogeneous barrier
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