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Found 4 papers, 1 papers with code
Statistical Edge Detection And UDF Learning For Shape Representation
In this paper, we propose a method for learning UDFs that improves the fidelity of the obtained Neural UDF to the original 3D surface.
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
The method is applicable to any classification model as it is applied directly in feature space at test time and does not intervene in training process.
Free Probability for predicting the performance of feed-forward fully connected neural networks
We present a reliable and very fast method for computing the associated spectral densities, for given architecture and initialization.
Geodesics in fibered latent spaces: A geometric approach to learning correspondences between conditions
This work introduces a geometric framework and a novel network architecture for creating correspondences between samples of different conditions.
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