Search Results for author:
Found 19 papers, 6 papers with code
Robust Principal Component Analysis?
This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted.
Information Theory Information Theory
Robust and computationally feasible community detection in the presence of arbitrary outlier nodes
To the best of the authors' knowledge, our result is the first in the literature in terms of clustering communities with fast growing numbers under the GSBM where a portion of arbitrary outlier nodes exist.
Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow
This paper considers the noisy sparse phase retrieval problem: recovering a sparse signal $x \in \mathbb{R}^p$ from noisy quadratic measurements $y_j = (a_j' x )^2 + \epsilon_j$, $j=1, \ldots, m$, with independent sub-exponential noise $\epsilon_j$.
Convexified Modularity Maximization for Degree-corrected Stochastic Block Models
We establish non-asymptotic theoretical guarantees for both approximate clustering and perfect clustering.
Subspace Perspective on Canonical Correlation Analysis: Dimension Reduction and Minimax Rates
Canonical correlation analysis (CCA) is a fundamental statistical tool for exploring the correlation structure between two sets of random variables.
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation
Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
Only the base images are submitted randomly to the cloud server.
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).
Single Reference Image based Scene Relighting via Material Guided Filtering
We propose a novel outdoor scene relighting method, which needs only a single reference image and is based on material constrained layer decomposition.
Multi-level Chaotic Maps for 3D Textured Model Encryption
In this work, a multi-level chaotic maps models for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons and textures.
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA
This work studies low-rank approximation of a positive semidefinite matrix from partial entries via nonconvex optimization.
Convex Relaxation Methods for Community Detection
We introduce some important theoretical techniques and results for establishing the consistency of convex community detection under various statistical models.
Nonconvex Rectangular Matrix Completion via Gradient Descent without $\ell_{2,\infty}$ Regularization
The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience.
Facial Makeup Transfer Combining Illumination Transfer
To meet the women appearance needs, we present a novel virtual experience approach of facial makeup transfer, developed into windows platform application software.
Aesthetic Attributes Assessment of Images
This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute.
Cosmological parameter estimation from large-scale structure deep learning
We propose a light-weight deep convolutional neural network to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology
Nonconvex Matrix Completion with Linearly Parameterized Factors
Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones.
Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems
In this paper, we examine the generalization capability of a machine learning model for problem reduction on the classic travelling salesman problems (TSP).
Boosting Ant Colony Optimization via Solution Prediction and Machine Learning
In the first phase of our ML-ACO algorithm, an ML model is trained using a set of small problem instances where the optimal solution is known.
