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stat.ML2023
Implicit regularization of deep residual networks towards neural ODEs
Pierre Marion, Yu-Han Wu, Michael E. Sander +1
Residual neural networks are state-of-the-art deep learning models. Their continuous-depth analog, neural ordinary differential equations (ODEs), are also widely used. Despite thei…
cs.LG2023
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
Michael E. Sander, Joan Puigcerver, Josip Djolonga +2
The top-k operator returns a sparse vector, where the non-zero values correspond to the k largest values of the input. Unfortunately, because it is a discontinuous function, it is…