4 papers
Graph Kernel Neural Networks
Luca Cosmo, Giorgia Minello, Alessandro Bicciato +4
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is rea…
Coupled quasi-harmonic bases
A. Kovnatsky, M. M. Bronstein, A. M. Bronstein +2
The use of Laplacian eigenbases has been shown to be fruitful in many computer graphics applications. Today, state-of-the-art approaches to shape analysis, synthesis, and correspon…
Functional correspondence by matrix completion
Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson +1
In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional corres…
On the Limitations of Fractal Dimension as a Measure of Generalization
Charlie B. Tan, Inés GarcÃa-Redondo, Qiquan Wang +2
Bounding and predicting the generalization gap of overparameterized neural networks remains a central open problem in theoretical machine learning. There is a recent and growing bo…