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Found 2 papers, 2 papers with code
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
How can agents learn internal models that veridically represent interactions with the real world is a largely open question.
Uncertainty estimation under model misspecification in neural network regression
Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the hypothesis class they are parameterizing.
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