
Robust Gaussian Process Regression with a Bias Model
This paper presents a new approach to a robust Gaussian process (GP) reg...
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Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations
A dimension reduction method based on the "Nonlinear Level set Learning"...
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Lung nodule segmentation via level set machine learning
Lung cancer has the highest mortality rate of all cancers in both men an...
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Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
Artificial Neural Networks form the basis of very powerful learning meth...
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Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under HeavyTailed Gradient Noise
Stochastic gradient descent with momentum (SGDm) is one of the most popu...
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Transformed Residual Quantization for Approximate Nearest Neighbor Search
The success of product quantization (PQ) for fast nearest neighbor searc...
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αVariational Inference with Statistical Guarantees
We propose a variational approximation to Bayesian posterior distributio...
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Relevant Ensemble of Trees
Tree ensembles are flexible predictive models that can capture relevant ...
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Frequentist coverage and supnorm convergence rate in Gaussian process regression
Gaussian process (GP) regression is a powerful interpolation technique d...
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Land Cover Classification from Multitemporal, Multispectral Remotely Sensed Imagery using PatchBased Recurrent Neural Networks
Sustainability of the global environment is dependent on the accurate la...
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Patchwork Kriging for Largescale Gaussian Process Regression
This paper presents a new approach for Gaussian process (GP) regression ...
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Bayesian model selection consistency and oracle inequality with intractable marginal likelihood
In this article, we investigate large sample properties of model selecti...
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On Statistical Optimality of Variational Bayes
The article addresses a longstanding open problem on the justification ...
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TwoLevel Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images
This paper presents a regularized regression model with a twolevel stru...
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Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background
This paper presents a robust regression approach for image binarization ...
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Parameterized Principal Component Analysis
When modeling multivariate data, one might have an extra parameter of co...
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Optimal Filtered Backprojection for Fast and Accurate Tomography Reconstruction
Tomographic reconstruction is a method of reconstructing a high dimensio...
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Bayesian Clustering of Shapes of Curves
Unsupervised clustering of curves according to their shapes is an import...
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Elastic Functional Coding of Riemannian Trajectories
Visual observations of dynamic phenomena, such as human actions, are oft...
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Feature Selection with Annealing for Computer Vision and Big Data Learning
Many computer vision and medical imaging problems are faced with learnin...
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The Artificial Regression Market
The Artificial Prediction Market is a recent machine learning technique ...
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RENOIR  A Dataset for Real LowLight Image Noise Reduction
Image denoising algorithms are evaluated using images corrupted by artif...
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Joint variable and rank selection for parsimonious estimation of highdimensional matrices
We propose dimension reduction methods for sparse, highdimensional mult...
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An Introduction to Artificial Prediction Markets for Classification
Prediction markets are used in real life to predict outcomes of interest...
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Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks ...
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LATTE: Application Oriented Network Embedding
In recent years, many research works propose to embed the networked data...
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BLECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion
Employees in companies can be divided into di erent communities, and tho...
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BLMNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
Network embedding aims at projecting the network data into a lowdimensi...
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Functional Decomposition using Principal Subfields
Let f∈ K(t) be a univariate rational function. It is well known that any...
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ReductionBased Creative Telescoping for Fuchsian Dfinite Functions
Continuing a series of articles in the past few years on creative telesc...
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Hypergeometric Expressions for Generating Functions of Walks with Small Steps in the Quarter Plane
We study nearestneighbors walks on the twodimensional square lattice, ...
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Computing Hypergeometric Solutions of Second Order Linear Differential Equations using Quotients of Formal Solutions and Integral Bases
We present two algorithms for computing hypergeometric solutions of seco...
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The Complexity of Computing all Subfields of an Algebraic Number Field
For a finite separable field extension K/k, all subfields can be obtaine...
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Macros to Conduct Tests of Multimodality in SAS
The Dip Test of Unimodality and Silverman's Critical Bandwidth Test are ...
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Online Regression with Model Selection
Online learning algorithms have a wide variety of applications in large ...
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ShapeConstrained Univariate Density Estimation
While the problem of estimating a probability density function (pdf) fro...
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Unsupervised Learning of Mixture Models with a Uniform Background Component
Gaussian Mixture Models are one of the most studied and mature models in...
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Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks
Artificial neural networks (ANNs) may not be worth their computational/m...
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CovariateAdjusted Tensor Classification in HighDimensions
In contemporary scientific research, it is of great interest to predict ...
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Robust Comparison of Kernel Densities on Spherical Domains
While spherical data arises in many contexts, including in directional s...
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GEN Model: An Alternative Approach to Deep Neural Network Models
In this paper, we introduce an alternative approach, namely GEN (Genetic...
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Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models
In this paper, we aim at introducing a new machine learning model, namel...
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Deep Loopy Neural Network Model for Graph Structured Data Representation Learning
Existing deep learning models may encounter great challenges in handling...
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On Deep Ensemble Learning from a Function Approximation Perspective
In this paper, we propose to provide a general ensemble learning framewo...
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Fake News Detection with Deep Diffusive Network Model
In recent years, due to the booming development of online social network...
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EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model
Programming has been an important skill for researchers and practitioner...
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TICNN: Convolutional Neural Networks for Fake News Detection
With the development of social networks, fake news for various commercia...
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Automatic Traceability Maintenance via Machine Learning Classification
Previous studies have shown that software traceability, the ability to l...
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To Cluster, or Not to Cluster: An Analysis of Clusterability Methods
Clustering is an essential data mining tool that aims to discover inhere...
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Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for NonConvex Stochastic Optimization: NonAsymptotic Performance Bounds and MomentumBased Acceleration
Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stoc...
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Florida State University is a public spacegrant and seagrant research university in Tallahassee, Florida.