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		<title>Why Machines Overfit: New Survey Brings Order to the Chaos of Regularization</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[benchmarking]]></category>
		<category><![CDATA[consistency principle]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[empirical risk minimization]]></category>
		<category><![CDATA[ill-posed problems]]></category>
		<category><![CDATA[loss functions]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[model generalization]]></category>
		<category><![CDATA[regularization]]></category>
		<category><![CDATA[sparsity]]></category>
		<category><![CDATA[taxonomy]]></category>
		<category><![CDATA[Tikhonov regularization]]></category>
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					<description><![CDATA[A new survey in Machine Learning rebuilds the theory of regularization from ill-posed problems up and maps the modern landscape of loss-based regularizers for model generalization.]]></description>
		
		
		
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