40 citations · 69 across the 26 of their papers we have counts for
28 papers
Polynomial Time Algorithms for the Kadison-Singer Problem
Zhao Song, Song Yue
Marcus, Spielman, and Srivastava [MSS15] established the existence of Kadison--Singer partitions. We provide polynomial-time algorithms for the Kadison--Singer problem. For Hermiti…
An Improvement to the Upper Bound for Marton's Covering Conjecture
Zhao Song, Song Yue
Marton's covering conjecture studies finite sets in high-dimensional binary spaces whose pairwise sums create relatively few new elements. It predicts that every such set can be de…
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…
A Unified Masked Jigsaw Puzzle Framework for Vision and Language Models
Weixin Ye, Wei Wang, Yahui Liu +5
In federated learning, Transformer, as a popular architecture, faces critical challenges in defending against gradient attacks and improving model performance in both Computer Visi…
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…
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…
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…
Householder Projector for Unsupervised Latent Semantics Discovery
Yue Song, Jichao Zhang, Nicu Sebe +1
Generative Adversarial Networks (GANs), especially the recent style-based generators (StyleGANs), have versatile semantics in the structured latent space. Latent semantics discover…
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…
Orthogonal SVD Covariance Conditioning and Latent Disentanglement
Yue Song, Nicu Sebe, Wei Wang
Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities.…
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…
Batch-efficient EigenDecomposition for Small and Medium Matrices
Yue Song, Nicu Sebe, Wei Wang
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…
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality
Yue Song, Nicu Sebe, Wei Wang
Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities.…
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
Yue Song, Nicu Sebe, Wei Wang
The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-class variations are difficult to be captured. One notable research line uses the Global Covar…
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
Bin Ren, Yahui Liu, Yue Song +4
Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs…