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Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks.
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Causality
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
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Recognition in terra incognita
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Adaptive risk minimization: A meta-learning approach for tackling group shift
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Domain generalization with optimal transport and metric learning
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Reducing domain gap by reducing style bias, 2021
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On disentangled representations learned from correlated data
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