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We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data.
Studies in the logic of confirmation (i.)
Carl G Hempel · 1945
Earlier work this paper cites.
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Dan Roth · 1996
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Earlier work this paper cites.
Hempel’s raven paradox: A lacuna in the standard Bayesian solution
Peter B.M. Vranas · 2004
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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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Combining representation learning with logic for language processing
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ImageNet Large Scale Visual Recognition Challenge
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End-to-end differentiable proving
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Realistic evaluation of semi-supervised learning algorithms
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Modeling Relational Data with Graph Convolutional Networks
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A semantic loss function for deep learning with symbolic knowledge
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