A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations

17 December 2021

Papers citing "A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations"

10 / 10 papers shown

Title
GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation Boris Kriuk Matey Yordanov 35 0 0 02 May 2025
Unveiling Transformer Perception by Exploring Input Manifolds A. Benfenati Alfio Ferrara A. Marta Davide Riva Elisabetta Rocchetti 18 0 0 08 Oct 2024
Deep Learning as Ricci Flow Anthony Baptista Alessandro Barp Tapabrata Chakraborti Chris Harbron Ben D. MacArthur Christopher R. S. Banerji AI4CE 41 0 0 22 Apr 2024
A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on $n$ -dimensional Classes A. Benfenati A. Marta 24 1 0 09 Apr 2024
Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trains Mario Gleirscher A. Haxthausen J. Peleska 16 4 0 26 Jun 2023
Neural networks learn to magnify areas near decision boundaries Jacob A. Zavatone-Veth Sheng Yang Julian Rubinfien C. Pehlevan MLT AI4CE 20 6 0 26 Jan 2023
Origami in N dimensions: How feed-forward networks manufacture linear separability Christian Keup M. Helias 11 8 0 21 Mar 2022
A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes A. Benfenati A. Marta 3DPC 15 9 0 17 Dec 2021
Geometric deep learning on graphs and manifolds using mixture model CNNs Federico Monti Davide Boscaini Jonathan Masci Emanuele Rodolà Jan Svoboda M. Bronstein GNN 251 1,811 0 25 Nov 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 250 3,236 0 24 Nov 2016