Cover Image

Connecting Neural Models Latent Geometries with Relative Geodesic Representations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yu, Hanlin, Inal, Berfin, Arvanitidis, Georgios, Hauberg, Soren, Locatello, Francesco, Fumero, Marco
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Geometry and Topology via Multi-Chart Flows
by: Yu, Hanlin, et al.
Published: (2025)

Navigating the Latent Space Dynamics of Neural Models
by: Fumero, Marco, et al.
Published: (2025)

Extended Neural Contractive Dynamical Systems: On Multiple Tasks and Riemannian Safety Regions
by: Mohammadi, Hadi Beik, et al.
Published: (2024)

Latent Space Translation via Inverse Relative Projection
by: Maiorca, Valentino, et al.
Published: (2024)

Adaptive Sampling for Continuous Group Equivariant Neural Networks
by: Inal, Berfin, et al.
Published: (2024)

Out-of-Distribution Detection with Relative Angles
by: Demirel, Berker, et al.
Published: (2024)

Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature
by: Williams, Bernardo, et al.
Published: (2025)

Neural Contractive Dynamical Systems
by: Beik-Mohammadi, Hadi, et al.
Published: (2024)

Latent Functional Maps: a spectral framework for representation alignment
by: Fumero, Marco, et al.
Published: (2024)

Unifying Causal Representation Learning with the Invariance Principle
by: Yao, Dingling, et al.
Published: (2024)

Simultaneous Optimization of Geodesics and Fréchet Means
by: Rygaard, Frederik Möbius, et al.
Published: (2025)

Latent Space Translation via Semantic Alignment
by: Maiorca, Valentino, et al.
Published: (2023)

Monge SAM: Robust Reparameterization-Invariant Sharpness-Aware Minimization Based on Loss Geometry
by: Jacobsen, Albert Kjøller, et al.
Published: (2025)

Statistical and structural identifiability in representation learning
by: Nelson, Walter, et al.
Published: (2026)

MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging
by: Demirel, Berker, et al.
Published: (2025)

The Spacetime of Diffusion Models: An Information Geometry Perspective
by: Karczewski, Rafał, et al.
Published: (2025)

Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections
by: Miani, Marco, et al.
Published: (2024)

A Likely Geometry of Generative Models
by: Rygaard, Frederik Möbius, et al.
Published: (2025)

Staying on the Manifold: Geometry-Aware Noise Injection
by: Jacobsen, Albert Kjøller, et al.
Published: (2025)

Counterfactual Explanations via Riemannian Latent Space Traversal
by: Pegios, Paraskevas, et al.
Published: (2024)

Identifying Metric Structures of Deep Latent Variable Models
by: Syrota, Stas, et al.
Published: (2025)

Contact Wasserstein Geodesics for Non-Conservative Schrödinger Bridges
by: Testa, Andrea, et al.
Published: (2025)

Boomerang Distillation Enables Zero-Shot Model Size Interpolation
by: Kangaslahti, Sara, et al.
Published: (2025)

Marrying Causal Representation Learning with Dynamical Systems for Science
by: Yao, Dingling, et al.
Published: (2024)

Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
by: Huang, Shimeng, et al.
Published: (2026)

Causal Learning with the Invariance Principle
by: Montagna, Francesco, et al.
Published: (2026)

Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them
by: Roy, Hrittik, et al.
Published: (2025)

Learning Explicit Single-Cell Dynamics Using ODE Representations
by: von Bassewitz, Jan-Philipp, et al.
Published: (2025)

Decoder ensembling for learned latent geometries
by: Syrota, Stas, et al.
Published: (2024)

Controlling Transient Amplification Improves Long-horizon Rollouts
by: Pervez, Adeel, et al.
Published: (2026)

The Rate-Distortion-Polysemanticity Tradeoff in SAEs
by: Mencattini, Tommaso, et al.
Published: (2026)

Exploring Neural Network Landscapes: Star-Shaped and Geodesic Connectivity
by: Lin, Zhanran, et al.
Published: (2024)

High-dimensional Analysis of Synthetic Data Selection
by: Rezaei, Parham, et al.
Published: (2025)

Mechanistic Neural Networks for Scientific Machine Learning
by: Pervez, Adeel, et al.
Published: (2024)

The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
by: Yao, Dingling, et al.
Published: (2025)

Relative Geometry of Neural Forecasters: Linking Accuracy and Alignment in Learned Latent Geometry
by: Kucukahmetler, Deniz, et al.
Published: (2026)

Riemann$^2$: Learning Riemannian Submanifolds from Riemannian Data
by: Rozo, Leonel, et al.
Published: (2025)

Geometric Contact Flows: Contactomorphisms for Dynamics and Control
by: Testa, Andrea, et al.
Published: (2025)

Mechanistic PDE Networks for Discovery of Governing Equations
by: Pervez, Adeel, et al.
Published: (2025)

From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
by: Cannistraci, Irene, et al.
Published: (2023)