10 citations · 10 across the 4 of their papers we have counts for
5 papers
Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
Yannick Lunk, Atell Yehor Krasnopolsky, Damien Garreau +1
Understanding why deep neural networks make a given prediction is of great importance for their safe deployment. In computer vision, saliency maps, which highlight the image region…
Sparse Training of Neural Networks based on Multilevel Mirror Descent
Yannick Lunk, Sebastian J. Scott, Leon Bungert
We introduce a dynamic sparse training algorithm based on linearized Bregman iterations / mirror descent that exploits the naturally incurred sparsity by alternating between period…
Convergence of graph Dirichlet energies and graph Laplacians on intersecting manifolds of varying dimensions
Leon Bungert, Dejan Slepčev
We study -convergence of graph Dirichlet energies and spectral convergence of graph Laplacians on unions of intersecting manifolds of potentially different dimensions. Our inves…
Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs
Eloi Martinet, Leon Bungert
Shape optimization involves the minimization of a cost function defined over a set of shapes, often governed by a partial differential equation (PDE). In the absence of closed-form…
Nonlinear Power Method for Computing Eigenvectors of Proximal Operators and Neural Networks
Leon Bungert, Ester Hait-Fraenkel, Nicolas Papadakis +1
Neural networks have revolutionized the field of data science, yielding remarkable solutions in a data-driven manner. For instance, in the field of mathematical imaging, they have…