1 citations · 1 across the 10 of their papers we have counts for
10 papers
LieBN: Batch Normalization over Lie Groups
Ziheng Chen, Yue Song, Rui Wang +2
Manifold-valued measurements are prevalent in various machine learning tasks. Recent advances have extended Deep Neural Networks (DNNs) to operate on manifolds. These extensions ha…
Towards Robust EEG Decoding Based on Riemannian Self-Attention
Shaocheng Jin, Tao Zhou, Rui Wang +4
Brain-Computer Interface (BCI) based on electroencephalography (EEG) enables direct interaction between the brain and external environments and has significant applications in assi…
Weierstrass Positional Encoding for Vision Transformers
Zhihang Xin, Rui Wang, Xitong Hu +1
Vision Transformers have achieved remarkable success in computer vision, but their common use of learnable one-dimensional positional encodings weakens the inherent two-dimensional…
Riemannian Networks over Full-Rank Correlation Matrices
Ziheng Chen, Xiaojun Wu, Bernhard Schölkopf +1
Representations on the Symmetric Positive Definite (SPD) manifold have garnered significant attention across different applications. In contrast, the manifold of full-rank correlat…
Wasserstein-Aligned Hyperbolic Multi-View Clustering
Rui Wang, Yuting Jiang, Xiaoqing Luo +3
Multi-view clustering (MVC) aims to uncover the latent structure of multi-view data by learning view-common and view-specific information. Although recent studies have explored hyp…
Riemannian Batch Normalization: A Gyro Approach
Ziheng Chen, Xiao-Jun Wu, Bernhard Schölkopf +1
Normalization layers are crucial for deep learning, but their Euclidean formulations are inadequate for data on manifolds. On the other hand, many Riemannian manifolds in machine l…
Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry
Rui Wang, Shaocheng Jin, Ziheng Chen +2
Covariance matrices have proven highly effective across many scientific fields. Since these matrices lie within the Symmetric Positive Definite (SPD) manifold - a Riemannian space…
RMLR: Extending Multinomial Logistic Regression into General Geometries
Ziheng Chen, Yue Song, Rui Wang +2
Riemannian neural networks, which extend deep learning techniques to Riemannian spaces, have gained significant attention in machine learning. To better classify the manifold-value…
Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
Ziheng Chen, Yue Song, Xiao-Jun Wu +2
Global Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations.…
Fast and Stable Riemannian Metrics on SPD Manifolds via Cholesky Product Geometry
Ziheng Chen, Yue Song, Xiao-Jun Wu +1
Recent advances in Symmetric Positive Definite (SPD) matrix learning show that Riemannian metrics are fundamental to effective SPD neural networks. Motivated by this, we revisit th…