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

v1v2 (latest)

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

17 December 2021

ArXiv (abs)PDF HTML

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

10 / 10 papers shown

RNNs perform task computations by dynamically warping neural representations

RNNs perform task computations by dynamically warping neural representations

Arthur Pellegrino

140

3

0

03 Dec 2025

Emergent Riemannian geometry over learning discrete computations on continuous manifolds

Emergent Riemannian geometry over learning discrete computations on continuous manifolds

Arthur Pellegrino

143

2

0

28 Nov 2025

GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation

GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentation

331

2

0

02 May 2025

Unveiling Transformer Perception by Exploring Input Manifolds

Unveiling Transformer Perception by Exploring Input Manifolds

Elisabetta Rocchetti

425

0

0

08 Oct 2024

Deep Learning as Ricci Flow

Deep Learning as Ricci Flow

Anthony Baptista

Alessandro Barp

Tapabrata Chakraborti

Ben D. MacArthur

Christopher R. S. Banerji

403

4

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 singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on

n

-dimensional Classes

277

4

0

09 Apr 2024

Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4
Freight Trains

Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight TrainsInternational Workshop on Formal Techniques for Safety-Critical Systems (FTSS), 2023

Mario Gleirscher

211

6

0

26 Jun 2023

How does training shape the Riemannian geometry of neural network representations?

How does training shape the Riemannian geometry of neural network representations?

Jacob A. Zavatone-Veth

Julian Rubinfien

Cengiz Pehlevan

560

6

0

26 Jan 2023

Origami in N dimensions: How feed-forward networks manufacture linear
separability

Origami in N dimensions: How feed-forward networks manufacture linear separability

355

10

0

21 Mar 2022

A singular Riemannian geometry approach to Deep Neural Networks II.
Reconstruction of 1-D equivalence classes

A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes

243

10

0

17 Dec 2021

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