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Learning Invariant Representations using Mutual Information Regularization
<!--HTML-->Invariance of learned representations of neural networks against certain sensitive attributes of the input data is a desirable trait in many modern-day applications of machine learning, such as precision measurements in experimental high-energy physics and enforcing algorithmic fair...
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| Lenguaje: | eng |
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2019
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| Acceso en línea: | http://cds.cern.ch/record/2672020 |