Riemannian Representation Learning

Latest papers

13 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

ACAM-J-MARS: Multi-Atlas Riemannian Spectral Representations for Decoding Advanced Meditation States from fMRI
Oct 29, 2026Jian Wang, Winson F. Z. Yang, Wugeng Zheng +1Riemannian Representation Learning Neural Decoding
Meditation Research Program, Psychiatry, MGH and Harvard Medical School, Boston, MA, USA · Martinos Center for Biomedical Imaging, Radiology, MGH and Harvard Medical School, Boston, MA, USA · College of Engineering, Northeastern University, Boston, MA, USA
Euclidean Crowding-Driven Adaptive Euclidean-Hyperbolic Graph Learning
Oct 27, 2026Hao Chen, Jian Sun, Chuan Shi +1Riemannian Representation Learning Graph Representation Learning
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China · Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
Constant-Curvature Sliced Gromov-Wasserstein for Heterogeneous Cross-Curvature Alignment
Oct 5, 2026Shanglin Li, Wenjing Lu, Muyang Li +2Distribution Matching Geometric Representation Learning
BIFOLD, Germany · Technical University of Berlin, Germany · RIKEN AIP, Japan +4
The Implicit Bias of Hyperbolic Representation Learning for Multiclass Data: A Busemann Risk Perspective
Oct 5, 2026Xingrun Li, Sho Kuno, Yusuke Mukuta +2Riemannian Optimization Implicit Bias
The University of Tokyo · RIKEN
On the Geometry of Multimodal Saturation: Riemannian VICReg
Oct 5, 2026Nessim Ben Abbes, Duc Han Le, Sabri Mtibaa +1Self-Supervised Learning Geometric Representation Learning
Protectline · Télécom Paris
Minkowski Attractor Networks: Closed-Form Hyperbolic Flows for Visual Representations
Sep 29, 2026Zhongping JiVisual Representation Learning Hierarchical Representation Learning
Building Transformation Layers for Riemannian Neural Networks
Sep 28, 2026Ziheng ChenRiemannian Neural Networks Geometric Representation Learning
University of Trento · MPI for Intelligent Systems, Tübingen
$\mathbb{SL}(n)$ Representation Learning: An Intrinsic Mixed-Curvature Space with Higher Curvature Capacities and Deeper Order-Aware Composition
Sep 14, 2026Xingrun Li, Yusuke Mukuta, Xin Yang +2Finsler Geometry Graph Representation Learning
The University of Tokyo · Stanford University
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Aug 6, 2026Quanxin Wang, Xuanting Xie, Bingheng Li +4Link Prediction Few-Shot Learning
University of Electronic Science and Technology of China · 2Michigan State University · 3Singapore Management University +2
Riemannian Deep Learning: Modules, Networks, and Geometries
Jul 21, 2026Ziheng ChenRiemannian Neural Networks Geometric Representation Learning
Doctoral School in Information and Communication Technology
I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals
Jul 1, 2026Cheng He, Kunyu Peng, Shangen Han +3Cross-Subject EEG Decoding Electroencephalography
Laboratory of Neural Computing and Intelligent Perception, College of Information Engineering, Capital Normal University, Beijing, China · Karlsruhe Institute of Technology, Germany · School of Psychology, Capital Normal University, Beijing, China
Beyond Rigid Geometries: The Spline-Pullback Metric for Universal Diffeomorphic SPD Representation Learning
May 6, 2026Tushar Das, Subrata Dutta, Sarmistha Neogy +1Geometric Representation Learning Metric Learning
National Institute of Technology Jamshedpur · Jadavpur University · Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur
Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
Mar 20, 2026Haifang Cao, Yu Wang, Timing Li +2Ollivier-Ricci Curvature Riemannian Representation Learning
College of Intelligence and Computing, Tianjin University, Tianjin, China

Oct 29, 2026J/K move · Enter open · S save

ACAM-J-MARS: Multi-Atlas Riemannian Spectral Representations for Decoding Advanced Meditation States from fMRI

Jian Wang, Winson F. Z. Yang, Wugeng Zheng, Matthew D. Sacchet

Meditation Research Program, Psychiatry, MGH and Harvard Medical School, Boston, MA, USA · Martinos Center for Biomedical Imaging, Radiology, MGH and Harvard Medical School, Boston, MA, USA · College of Engineering, Northeastern University, Boston, MA, USA

Riemannian Representation Learning Neural Decoding fMRI Representation Learning Neuroimaging Functional Connectivity Geometric Representation Learning

Open paper AskPDF arXiv

Inferring jhana-type advanced concentration absorption meditation (ACAM-J) states from fMRI data is challenging due to the spatially distributed nature of meditation-related effects, substantial inter-individual variability that can exceed within-subject state differences, and limited sample sizes. Conventional machine-learning approaches often formulate this problem as unimodal, Euclidean, atlas-based classification, thereby neglecting the non-Euclidean geometry of functional connectivity matrices and underrepresenting temporal information in functional magnetic resonance imaging (fMRI); these limitations can lead to suboptimal classification performance. We propose ACAM-J-MARS (Multi-Atlas Riemannian Spectral representation), which computes functional connectivity using multiple atlases, represents symmetric positive definite (SPD) connectivity matrices on a Riemannian manifold, projects them into a log-Euclidean tangent space, and augments connectivity-derived features with low-frequency BOLD spectral energy to better resolve subtle state differences under heterogeneous variability. Evaluated on 204 segments from 22 practitioners, ACAM-J-MARS improves performance across multiple backbone models, achieving a balanced accuracy of 0.757 for early versus late ACAM-J classification and 0.545 for three-class classification, corresponding to a 10.8-point absolute improvement over the strongest baseline. These findings suggest that the proposed representation improves the decoding of advanced meditative brain states and may enable downstream applications such as closed-loop neurofeedback and real-time psychophysiological brain-computer interfaces.