From the 1 of 122 linked papers with an AI index.
21 citations · 29 across the 37 of their papers we have counts for
15 papers · 1 filter
LieBN: Batch Normalization over Lie Groups
Ziheng Chen, Yue Song, Rui Wang +2
The paper introduces LieBN, a batch‑normalization framework that works on data lying on Lie groups, providing theoretical control of Riemannian mean and variance across several com…
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…
The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery
Haiyang Zheng, Nan Pu, Yaqi Cai +4
Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly optimize supervised and uns…
Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosis
Chen Feng, Zhuo Zhi, Zhao Huang +5
Statistically consistent methods based on the noise transition matrix () offer a theoretically grounded solution to Learning with Noisy Labels (LNL), with guarantees of converge…
Hyperbolic Busemann Neural Networks
Ziheng Chen, Bernhard Schölkopf, Nicu Sebe
Hyperbolic spaces provide a natural geometry for representing hierarchical and tree-structured data due to their exponential volume growth. To leverage these benefits, neural netwo…
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…