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Found 31 papers, 11 papers with code
Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder
Today, there have been many achievements in learning the association between voice and face.
Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework
Besides, by personalized integration of domain features from other domains for each user and the innovation in the training mode, the DFEI framework can yield more accurate conversion identification.
Calibrating the Confidence of Large Language Models by Eliciting Fidelity
Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless.
Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction
Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients.
PRIME: Uncovering Circadian Oscillation Patterns and Associations with AD in Untimed Genome-wide Gene Expression across Multiple Brain Regions
Then, we apply it to study oscillation patterns in untimed genome-wide gene expression from 19 human brain regions of controls and AD patients.
Alcohol Intake Differentiates AD and LATE: A Telltale Lifestyle from Two Large-Scale Datasets
Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior.
A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection
The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames.
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations.
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively.
Hyperspectral Image Denoising with Log-Based Robust PCA
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs).
Kernel Two-Dimensional Ridge Regression for Subspace Clustering
It directly uses 2D data as inputs such that the learning of representations benefits from inherent structures and relationships of the data.
Structured Graph Learning for Clustering and Semi-supervised Classification
Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Consequently, we can achieve a significant performance improvement by using this new training set to start a new round of network training.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
Depth Quality Aware Salient Object Detection
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D).
Ranked #16 on
RGB-D Salient Object Detection
on NJU2K
Full Reference Screen Content Image Quality Assessment by Fusing Multi-level Structure Similarity
The screen content images (SCIs) usually comprise various content types with sharp edges, in which the artifacts or distortions can be well sensed by the vanilla structure similarity measurement in a full reference manner.
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions.
Structure Learning with Similarity Preserving
Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications.
Nonnegative Matrix Factorization with Local Similarity Learning
Existing nonnegative matrix factorization methods focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that commonly exists among data.
RES-PCA: A Scalable Approach to Recovering Low-rank Matrices
This drawback has limited the application of RPCA in solving real world problems.
Discriminative Ridge Machine: A Classifier for High-Dimensional Data or Imbalanced Data
As a special case we focus on a quadratic model that admits a closed-form analytical solution.
Unified Spectral Clustering with Optimal Graph
Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers.
Subspace Clustering via Variance Regularized Ridge Regression
Spectral clustering based subspace clustering methods have emerged recently.
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering.
Top-N Recommendation on Graphs
To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix.
A Fast Factorization-based Approach to Robust PCA
Our method can be used as a light-weight, scalable tool for RPCA in the absence of the precise value of the true rank.
Top-N Recommender System via Matrix Completion
Top-N recommender systems have been investigated widely both in industry and academia.
Robust PCA via Nonconvex Rank Approximation
This approximation to the matrix rank is tighter than the nuclear norm.
Robust Subspace Clustering via Tighter Rank Approximation
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
Robust Subspace Clustering via Smoothed Rank Approximation
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.
LogDet Rank Minimization with Application to Subspace Clustering
Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator.
