Riemannian Neural Networks

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10 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Building Transformation Layers for Riemannian Neural Networks
Sep 28, 2026Ziheng ChenRiemannian Neural Networks Geometric Representation Learning
University of Trento · MPI for Intelligent Systems, Tübingen
hyperbolix: Hyperbolic Deep Learning in JAX
Sep 23, 2026Timo Klein, Thomas Lang, Yllka Velaj +1Hyperbolic Representation Learning Riemannian Neural Networks
Faculty of Computer Science, University of Vienna, Vienna, Austria · UniVie Doctoral School Computer Science, University of Vienna, Vienna, Austria · ds:UniVie, University of Vienna, Vienna, Austria
1-Lipschitz Neural Networks on Hadamard Manifolds
Jul 21, 2026Davide Murari, Marta Ghirardelli, Ben Adcock +3Riemannian Neural Networks Neural Network Robustness
Department of Applied Mathematics and Theoretical Physics, University of Cambridge · Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU) · Department of Mathematics, Simon Fraser University
Riemannian Deep Learning: Modules, Networks, and Geometries
Jul 21, 2026Ziheng ChenRiemannian Neural Networks Geometric Representation Learning
Doctoral School in Information and Communication Technology, Università di Trento
Riemannian Networks over Full-Rank Correlation Matrices
May 18, 2026Ziheng Chen, Xiaojun Wu, Bernhard Schölkopf +1Riemannian Neural Networks Geometric Representation Learning
Department of Information Engineering and Computer Science, University of Trento, Trento, Italy · Max Planck Institute for Intelligent Systems, Tübingen, Germany · School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
Batch Normalization for Neural Networks on Complex Domains
May 1, 2026Xuan Son Nguyen, Nistor GrozavuRiemannian Neural Networks Complex-Valued Neural Networks
ETIS, UMR 8051, CY Cergy Paris University, ENSEA, CNRS, Cergy, France
MPNet: A Robust and Efficient Manifold Pooling Network for Multi-Rhythm EEG Signal Decoding
Apr 11, 2026Guoqing Cai, Kai Zeng, Shoulin Huang +1Riemannian Neural Networks EEG Decoding
Harbin Institute of Technology (Shenzhen), Shenzhen, China · Guangxi Normal University, Guilin, China · Peng Cheng Laboratory, Shenzhen, China
Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation
Mar 20, 2026Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière +4Riemannian Neural Networks Object Pose Estimation
CVI2, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg⋆ · Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France† · Infinite Orbits⋊ +1
ReManNet: A Riemannian Manifold Network for Monocular 3D Lane Detection
Mar 20, 2026Chengzhi Hong, Bijun LiRiemannian Neural Networks Autonomous Driving Perception
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
MSINO: Curvature-Aware Sobolev Optimization for Manifold Neural Networks
Feb 26, 2026Suresan ParethRiemannian Neural Networks Neural Network Optimization

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Building Transformation Layers for Riemannian Neural Networks

Ziheng Chen

University of Trento · MPI for Intelligent Systems, Tübingen

Riemannian Neural Networks Geometric Representation Learning

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Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. One recent focus is the generalization of Euclidean fully connected (FC) and convolutional layers to non-Euclidean geometries. However, previous approaches typically focus on a few selected manifolds and rely on specific properties of the target manifold. In contrast, this work proposes a framework for constructing FC and convolutional layers over computationally tractable Riemannian spaces. This framework incorporates several previous FC layers across different geometries as special cases and is instantiated on ten representative manifolds, including three hyperbolic models, five geometries of the symmetric positive definite (SPD) manifold, and two Grassmannian perspectives. Experiments on different manifolds demonstrate the effectiveness and applicability of our approach. Code can be found at https://github.com/GitZH-Chen/RieTrans.