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Found 45 papers, 6 papers with code
A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion
We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers.
Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator
It further shows that under the generalized haystack model, STE initialized by the Tyler's M-estimator (TME), can recover the subspace when the fraction of iniliers is too small for TME to handle.
Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching
Model fusion is becoming a crucial component in the context of model-as-a-service scenarios, enabling the delivery of high-quality model services to local users.
Improved Convergence Rates of Windowed Anderson Acceleration for Symmetric Fixed-Point Iterations
This paper studies the commonly utilized windowed Anderson acceleration (AA) algorithm for fixed-point methods, $x^{(k+1)}=q(x^{(k)})$.
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts.
FedBone: Towards Large-Scale Federated Multi-Task Learning
The conflicting gradient projection technique is used to enhance the generalization of the large-scale general model between different tasks.
Scalable Optimal Margin Distribution Machine
Optimal margin Distribution Machine (ODM) is a newly proposed statistical learning framework rooting in the novel margin theory, which demonstrates better generalization performance than the traditional large margin based counterparts.
Understanding Overfitting in Adversarial Training via Kernel Regression
Adversarial training and data augmentation with noise are widely adopted techniques to enhance the performance of neural networks.
Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net
Sparse principal component analysis (SPCA) is widely used for dimensionality reduction and feature extraction in high-dimensional data analysis.
Robust Regularized Low-Rank Matrix Models for Regression and Classification
While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued predictors.
On the Optimization of Margin Distribution
Margin has played an important role on the design and analysis of learning algorithms during the past years, mostly working with the maximization of the minimum margin.
The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling
In order for an algorithm to see sustained use, it must be compatible with changes to hospital capacity, patient volumes, and scheduling practices.
Distributed Optimal Margin Distribution Machine
Optimal margin Distribution Machine (ODM), a newly proposed statistical learning framework rooting in the novel margin theory, demonstrates better generalization performance than the traditional large margin based counterparts.
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers
As a result, our theoretical proofs provide support to more efficient active learning methods with the help of adversarial examples, contrary to previous works where adversarial examples are often used as destructive solutions.
FaceCook: Face Generation Based on Linear Scaling Factors
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images.
Understanding and controlling hexagonal patterns of wrinkles in neo-Hookean elastic bilayer structures
We employ large-scale finite element simulations of a bilayer neo-Hookean solid (e. g., a film bonded on a substrate) to explore mechanical principles that govern the formation of hexagonal wrinkling patterns and strategies for making nearly perfect hexagonal patterns.
Soft Condensed Matter
ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network
The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model.
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction
We convert the Chinese medical text attributes extraction task into a sequence tagging or machine reading comprehension task.
Faces à la Carte: Text-to-Face Generation via Attribute Disentanglement
Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications.
Memory Augmented Generative Adversarial Networks for Anomaly Detection
Decoded memory units in MEMGAN are more interpretable and disentangled than previous methods, which further demonstrates the effectiveness of the memory mechanism.
To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?
Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.
An Algorithm for Graph-Fused Lasso Based on Graph Decomposition
In comparison, this work proposes to decompose the objective function into two components, where one component is the loss function plus part of the total variation penalty, and the other component is the remaining total variation penalty.
Optimization and Control Computation
Deep inspection: an electrical distribution pole parts study via deep neural networks
We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest.
CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels
This is because the system can ignore the attention mechanism by assigning equal weights for all members.
Deep Instance-Level Hard Negative Mining Model for Histopathology Images
Histopathology image analysis can be considered as a Multiple instance learning (MIL) problem, where the whole slide histopathology image (WSI) is regarded as a bag of instances (i. e, patches) and the task is to predict a single class label to the WSI.
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks
In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way.
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
However, in addition to the text data itself, we often have additional covariates associated with individual corpus documents---e. g. the demographic of the author, time and venue of publication---and we would like the embedding to naturally capture this information.
Learning Covariate-Specific Embeddings with Tensor Decompositions
In addition to the text data itself, we often have additional covariates associated with individual documents in the corpus---e. g. the demographic of the author, time and venue of publication, etc.---and we would like the embedding to naturally capture the information of the covariates.
TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition
While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD).
Exact Camera Location Recovery by Least Unsquared Deviations
We establish exact recovery for the Least Unsquared Deviations (LUD) algorithm of Ozyesil and Singer.
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization.
Multi-Class Optimal Margin Distribution Machine
It still remains open for multi-class classification, and due to the complexity of margin for multi-class classification, optimizing its distribution by mean and variance can also be difficult.
Robust PCA by Manifold Optimization
Robust PCA is a widely used statistical procedure to recover a underlying low-rank matrix with grossly corrupted observations.
The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge.
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery
The practicality of the deterministic condition is demonstrated on some statistical models of data, and the method achieves almost state-of-the-art recovery guarantees on the Haystack Model for different regimes of sample size and ambient dimension.
Spectral clustering in the dynamic stochastic block model
We estimate the edge probability tensor by a kernel-type procedure and extract the group memberships of the nodes by spectral clustering.
Anisotropic twicing for single particle reconstruction using autocorrelation analysis
The missing phase problem in X-ray crystallography is commonly solved using the technique of molecular replacement, which borrows phases from a previously solved homologous structure, and appends them to the measured Fourier magnitudes of the diffraction patterns of the unknown structure.
Optimal Margin Distribution Machine
Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i. e., the smallest distance from the instances to the classification boundary.
Denoising and Covariance Estimation of Single Particle Cryo-EM Images
In CWF, the covariance matrix of the projection images is used within the classical Wiener filtering framework for solving the image restoration deconvolution problem.
Orthogonal Matrix Retrieval in Cryo-Electron Microscopy
In single particle reconstruction (SPR) from cryo-electron microscopy (cryo-EM), the 3D structure of a molecule needs to be determined from its 2D projection images taken at unknown viewing directions.
Large Margin Distribution Machine
In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin distribution.
Robust subspace recovery by Tyler's M-estimator
This paper considers the problem of robust subspace recovery: given a set of $N$ points in $\mathbb{R}^D$, if many lie in a $d$-dimensional subspace, then can we recover the underlying subspace?
Robust computation of linear models by convex relaxation
Consider a dataset of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers.
A Novel M-Estimator for Robust PCA
That is, we assume a data set that some of its points are sampled around a fixed subspace and the rest of them are spread in the whole ambient space, and we aim to recover the fixed underlying subspace.
lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers
We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces.
