Cover Image

Riemannian Batch Normalization: A Gyro Approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Ziheng, Wu, Xiao-Jun, Schölkopf, Bernhard, Sebe, Nicu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Lie Group Approach to Riemannian Batch Normalization
by: Chen, Ziheng, et al.
Published: (2024)

Riemannian Networks over Full-Rank Correlation Matrices
by: Chen, Ziheng, et al.
Published: (2026)

Hyperbolic Busemann Neural Networks
by: Chen, Ziheng, et al.
Published: (2026)

Intrinsic Lorentz Neural Network
by: Shi, Xianglong, et al.
Published: (2026)

RMLR: Extending Multinomial Logistic Regression into General Geometries
by: Chen, Ziheng, et al.
Published: (2024)

Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
by: Chen, Ziheng, et al.
Published: (2024)

Discriminative Anchor Learning for Efficient Multi-view Clustering
by: Qin, Yalan, et al.
Published: (2024)

Fast and Stable Riemannian Metrics on SPD Manifolds via Cholesky Product Geometry
by: Chen, Ziheng, et al.
Published: (2024)

A Probabilistic Model Behind Self-Supervised Learning
by: Bizeul, Alice, et al.
Published: (2024)

An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem
by: Guo, Runze, et al.
Published: (2024)

Riemannian Multinomial Logistics Regression for SPD Neural Networks
by: Chen, Ziheng, et al.
Published: (2023)

Making Batch Normalization Great in Federated Deep Learning
by: Zhong, Jike, et al.
Published: (2023)

Causality can systematically address the monsters under the bench(marks)
by: Leeb, Felix, et al.
Published: (2025)

Out-of-Variable Generalization for Discriminative Models
by: Guo, Siyuan, et al.
Published: (2023)

Computational Arbitrage in AI Model Markets
by: Olmedo, Ricardo, et al.
Published: (2026)

Batch Normalization Amplifies Memorization and Privacy Risks
by: Doan, Ngoc Phu, et al.
Published: (2026)

Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering
by: Kladny, Klaus-Rudolf, et al.
Published: (2024)

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
by: Palenicek, Daniel, et al.
Published: (2025)

StableGrad: Backward Scale Control without Batch Normalization
by: Mestre, Jose I., et al.
Published: (2026)

Preference Elicitation for Offline Reinforcement Learning
by: Pace, Alizée, et al.
Published: (2024)

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery
by: Zheng, Haiyang, et al.
Published: (2026)

Provable Privacy with Non-Private Pre-Processing
by: Hu, Yaxi, et al.
Published: (2024)

Identifying Intervenable and Interpretable Features via Orthogonality Regularization
by: Miller, Moritz, et al.
Published: (2026)

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations
by: Paischer, Fabian, et al.
Published: (2025)

Stargazer: A Scalable Model-Fitting Benchmark Environment for AI Agents under Astrophysical Constraints
by: Liu, Xinge, et al.
Published: (2026)

Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
by: Kekić, Armin, et al.
Published: (2025)

Natural Building Blocks for Structured World Models: Theory, Evidence, and Scaling
by: Da Costa, Lancelot, et al.
Published: (2025)

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
by: Reizinger, Patrik, et al.
Published: (2024)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)

Adaptive Log-Euclidean Metrics for SPD Matrix Learning
by: Chen, Ziheng, et al.
Published: (2023)

Masked Image Modeling: A Survey
by: Hondru, Vlad, et al.
Published: (2024)

Intrinsically Interpretable Attention via Sparse Post-Training
by: Draye, Florent, et al.
Published: (2025)

Improving Large Language Model Safety with Contrastive Representation Learning
by: Simko, Samuel, et al.
Published: (2025)

Counterfactual reasoning: an analysis of in-context emergence
by: Miller, Moritz, et al.
Published: (2025)

Algorithmic causal structure emerging through compression
by: Wendong, Liang, et al.
Published: (2025)

Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection
by: Wu, Yinting, et al.
Published: (2024)

Causal vs. Anticausal merging of predictors
by: Mejia, Sergio Hernan Garrido, et al.
Published: (2025)

Back To The Future: A Hybrid Transformer-XGBoost Model for Action-oriented Future-proofing Nowcasting
by: Sun, Ziheng
Published: (2024)

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning
by: Reizinger, Patrik, et al.
Published: (2025)

Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
by: Binkyte, Ruta, et al.
Published: (2025)