Cover Image

Intrinsic Gaussian Vector Fields on Manifolds

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Robert-Nicoud, Daniel, Krause, Andreas, Borovitskiy, Viacheslav
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Residual Deep Gaussian Processes on Manifolds
by: Wyrwal, Kacper, et al.
Published: (2024)

Implicit Manifold Gaussian Process Regression
by: Fichera, Bernardo, et al.
Published: (2023)

Hodge-Compositional Edge Gaussian Processes
by: Yang, Maosheng, et al.
Published: (2023)

Heat and Matérn Kernels on Matchings
by: Eremeev, Dmitry, et al.
Published: (2026)

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
by: Azangulov, Iskander, et al.
Published: (2022)

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
by: Azangulov, Iskander, et al.
Published: (2023)

Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?
by: Vieira, Pedro C., et al.
Published: (2026)

Matérn Gaussian Processes on Graphs
by: Borovitskiy, Viacheslav, et al.
Published: (2020)

Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
by: Doumont, Colin, et al.
Published: (2025)

Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data
by: Fichera, Bernardo, et al.
Published: (2026)

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
by: Mostowsky, Peter, et al.
Published: (2024)

Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
by: Jaquier, Noémie, et al.
Published: (2022)

Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
by: Ye, Ke, et al.
Published: (2020)

Intrinsic Gaussian Process Regression Modeling for Manifold-valued Response Variable
by: Wang, Zhanfeng, et al.
Published: (2024)

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
by: Atanackovic, Lazar, et al.
Published: (2024)

Risk-averse policies for natural gas futures trading using distributional reinforcement learning
by: Hêche, Félicien, et al.
Published: (2025)

Discrete Gaussian Vector Fields On Meshes
by: Gillan, Michael, et al.
Published: (2025)

Vector Quantile Regression on Manifolds
by: Pegoraro, Marco, et al.
Published: (2023)

Provable Maximum Entropy Manifold Exploration via Diffusion Models
by: De Santi, Riccardo, et al.
Published: (2025)

Manifold Diffusion Fields
by: Elhag, Ahmed A., et al.
Published: (2023)

LITE: Efficiently Estimating Gaussian Probability of Maximality
by: Menet, Nicolas, et al.
Published: (2025)

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds
by: Fu, Guoji, et al.
Published: (2026)

An Intrinsic Vector Heat Network
by: Gao, Alexander, et al.
Published: (2024)

Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds
by: Li, Yibang, et al.
Published: (2026)

Gaussian Process on the Product of Directional Manifolds
by: Cao, Ziyu, et al.
Published: (2023)

Chain-structured neural architecture search for financial time series forecasting
by: Levchenko, Denis, et al.
Published: (2024)

Optimistic Online LQR via Intrinsic Rewards
by: Bartos, Marcell, et al.
Published: (2026)

Manifold Gaussian Variational Bayes on the Precision Matrix
by: Magris, Martin, et al.
Published: (2022)

Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectors
by: Sinii, Viacheslav, et al.
Published: (2025)

A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis
by: Bi, Zelong, et al.
Published: (2025)

The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning
by: Holmgren, Benjamin, et al.
Published: (2024)

Physical knowledge improves prediction of EM Fields
by: Dulny, Andrzej, et al.
Published: (2025)

Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
by: Barkov, Viacheslav, et al.
Published: (2025)

Vector Optimization with Gaussian Process Bandits
by: Korkmaz, İlter Onat, et al.
Published: (2024)

Machine Learning (ML) library in Linux kernel
by: Dubeyko, Viacheslav
Published: (2026)

Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
by: Huang, Jiawei, et al.
Published: (2024)

Probabilistic Artificial Intelligence
by: Krause, Andreas, et al.
Published: (2025)

Gaussian Mixture Vector Quantization with Aggregated Categorical Posterior
by: Yan, Mingyuan, et al.
Published: (2024)

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization
by: Puppala, Sai, et al.
Published: (2025)

Active Learning for Manifold Gaussian Process Regression
by: Cheng, Yuanxing, et al.
Published: (2025)