40 citations · 69 across the 26 of their papers we have counts for
13 papers · 1 filter
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…
The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model
Zijie Yu, Gaowen Liu, Ramana Rao Kompella +2
Contrastive Language-Image Pretraining (CLIP) representations form a semantic embedding space governed by cosine similarity, reflecting an intrinsic hyperspherical geometry. Howeve…
A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition
Yue Song
EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particular…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
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…
A Lie Group Approach to Riemannian Batch Normalization
Ziheng Chen, Yue Song, Yunmei Liu +1
Manifold-valued measurements exist in numerous applications within computer vision and machine learning. Recent studies have extended Deep Neural Networks (DNNs) to manifolds, and…
RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detection
Yue Song, Wei Wang, Nicu Sebe
The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings. In this paper, we observe that the singular value distribut…
Flow Factorized Representation Learning
Yue Song, T. Anderson Keller, Nicu Sebe +1
A prominent goal of representation learning research is to achieve representations which are factorized in a useful manner with respect to the ground truth factors of variation. Th…
Riemannian Multinomial Logistics Regression for SPD Neural Networks
Ziheng Chen, Yue Song, Gaowen Liu +3
Deep neural networks for learning Symmetric Positive Definite (SPD) matrices are gaining increasing attention in machine learning. Despite the significant progress, most existing S…
Structured and Fast Optimization: The Kronecker SGD Algorithm
Zhao Song, Song Yue
Stochastic gradient descent (SGD) now acts as a fundamental part of optimization in current machine learning. Meanwhile, deep learning architectures have shown outstanding performa…
Latent Traversals in Generative Models as Potential Flows
Yue Song, T. Anderson Keller, Nicu Sebe +1
Despite the significant recent progress in deep generative models, the underlying structure of their latent spaces is still poorly understood, thereby making the task of performing…
Adaptive Log-Euclidean Metrics for SPD Matrix Learning
Ziheng Chen, Yue Song, Tianyang Xu +3
Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity to encode underlying structural correlation in data. Man…
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
Yue Song, Nicu Sebe, Wei Wang
The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings. In this paper, we observe that the singular value distribut…