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cs.LG2024
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms
Felix Petersen, Christian Borgelt, Tobias Sutter +3
When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular appr…
cs.LG2024
Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Mishra, Hilde Kuehne +3
We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal appro…