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Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty
Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer.
Improving Neural Network Accuracy and Calibration Under Distributional Shift with Prior Augmented Data
We demonstrate the effectiveness of our method and validate its performance on both classification and regression problems by applying it to the training of recent state-of-the-art neural network models.
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