Search Results for author:
Found 21 papers, 3 papers with code
Toward Faster and Simpler Matrix Normalization via Rank-1 Update
To boost the efficiency in the GPU platform, recent methods rely on Newton-Schulz (NS) iteration to approximate the matrix square-root.
SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior
To tackle this problem, we propose a novel approach that enhances the capacity of 3DGS by leveraging prior from a Diffusion Model along with complementary multi-modal data.
Neural Field Classifiers via Target Encoding and Classification Loss
We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks.
MQuinE: a cure for "Z-paradox" in knowledge graph embedding models
Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval.
Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrix
In this paper, we first propose a DAG structure recovering algorithm, which is based on the Cholesky factorization of the covariance matrix of the observed data.
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images.
SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space
Translation distance based knowledge graph embedding (KGE) methods, such as TransE and RotatE, model the relation in knowledge graphs as translation or rotation in the vector space.
On the Power-Law Hessian Spectrums in Deep Learning
It is well-known that the Hessian of deep loss landscape matters to optimization, generalization, and even robustness of deep learning.
A Note on Sparse Generalized Eigenvalue Problem
The sparse generalized eigenvalue problem (SGEP) aims to find the leading eigenvector with sparsity structure.
Causal Discovery via Cholesky Factorization
Discovering the causal relationship via recovering the directed acyclic graph (DAG) structure from the observed data is a challenging combinatorial problem.
Constructing Orthogonal Convolutions in an Explicit Manner
A convolution layer with an orthogonal Jacobian matrix is 1-Lipschitz in the 2-norm, making the output robust to the perturbation in input.
S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision
More recently, using smaller patches with a pyramid structure, Vision Permutator (ViP) and Global Filter Network (GFNet) achieve better performance than S$^2$-MLP.
Rethinking Token-Mixing MLP for MLP-based Vision Backbone
By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone.
S$^2$-MLP: Spatial-Shift MLP Architecture for Vision
We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration.
Ratio Trace Formulation of Wasserstein Discriminant Analysis
We reformulate the Wasserstein Discriminant Analysis (WDA) as a ratio trace problem and present an eigensolver-based algorithm to compute the discriminative subspace of WDA.
Identification of Matrix Joint Block Diagonalization
This paper considers the identification problem for BJBDP, that is, under what conditions and by what means, we can identify the diagonalizer $A$ and the block diagonal structure of $\Sigma_i$, especially when there is noise in $C_i$'s.
Tensor Completion via Tensor Networks with a Tucker Wrapper
In this paper, we propose to solve LRTC via tensor networks with a Tucker wrapper.
An Inverse-free Truncated Rayleigh-Ritz Method for Sparse Generalized Eigenvalue Problem
Particularly, a new truncation strategy is proposed, which is able to find the support set of the leading eigenvector effectively.
Solving the Robust Matrix Completion Problem via a System of Nonlinear Equations
We consider the problem of robust matrix completion, which aims to recover a low rank matrix $L_*$ and a sparse matrix $S_*$ from incomplete observations of their sum $M=L_*+S_*\in\mathbb{R}^{m\times n}$.
Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition
To enhance the generalization ability of extracted features, a novel relaxed 2DPCA (R2DPCA) is proposed with a new ridge regression model.
Relaxed 2-D Principal Component Analysis by $L_p$ Norm for Face Recognition
A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition.
