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Supervised maximum variance unfolding
Maximum Variance Unfolding (MVU) is among the first methods in nonlinear dimensionality reduction for data visualization and classification. It aims...
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Quantum mechanics-based deep learning framework considering near-zero variance data
AbstractWith the development of automation technology, big data is collected during operation processes, and among various machine learning analysis...
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Variance Reduction in Ratio Metrics for Efficient Online Experiments
Online controlled experiments, such as A/B-tests, are commonly used by modern tech companies to enable continuous system improvements. Despite their...
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Variance reduction for Metropolis–Hastings samplers
We introduce a general framework that constructs estimators with reduced variance for random walk Metropolis and Metropolis-adjusted Langevin...
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Ranking Variance Reduced Ensemble Attack with Dual Optimization Surrogate Search
Deep neural networks have achieved remarkable success, but they are vulnerable to adversarial attacks. Previous studies have shown that combining...
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Variance-based no-reference quality assessment of AWGN images
In this paper, a no-reference quality assessment method for image contaminated with additive white Gaussian noise (AWGN) is proposed. The proposed...
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Lead ASR Models to Generalize Better Using Approximated Bias-Variance Tradeoff
The conventional recipe for Automatic Speech Recognition (ASR) models is to 1) train multiple checkpoints on a training set while relying on a...
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Adaptive online variance estimation in particle filters: the ALVar estimator
We present a new approach—the
ALVar estimator—to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The...
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Lung Cancer Detection by Employing Adaptive Entropy Variance Dropout Regularization in GAN Variants
Lung cancer segmentation using Deep Neural Networks (DNN) needs accurate pixel-level data which is typically small. This leads to overfitting issue,...
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Stochastic variance reduced gradient with hyper-gradient for non-convex large-scale learning
Non-convex optimization, which can better capture the problem structure, has received considerable attention in the applications of machine learning,...
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Mean–variance scaling and stability in commercial sex work networks
Understanding how networks change over time can help identify network properties related to stability and uncover general scaling rules of network...
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The variance entropy multi-level thresholding method
This paper proposes a new multi-level entropy-based image thresholding method. The key principle of the proposed method depends on the minimum of the...
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An adaptive variance vector-based evolutionary algorithm for large scale multi-objective optimization
Large scale multi-objective optimization problems often involve hundreds or thousands of decision variables. Regular methods tend to divide decision...
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Interpretable linear dimensionality reduction based on bias-variance analysis
One of the central issues of several machine learning applications on real data is the choice of the input features. Ideally, the designer should...
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Multiclass variance based variational decomposition system for image segmentation
Thresholding-based approaches are widely used for image segmentation due to their low computational cost and complexity and ease of implementation....
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A projected decentralized variance-reduction algorithm for constrained optimization problems
Solving constrained optimization problems that require processing large-scale data is of significant value in practical applications, and such...
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MVM-LBP : mean−variance−median based LBP for face recognition
This paper proposes a novel descriptor called Mean-Variance-Median based Local binary pattern (MVM-LBP). The Median binary pattern (MBP) calculates...
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Improving the segmentation of digital images by using a modified Otsu’s between-class variance
Image segmentation is a critical stage in the analysis and pre-processing of images. It comprises dividing the pixels according to threshold values...
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Fuzzy and non-fuzzy k-quantile clustering for high-variance data
Clustering methods are algorithms that identify similar data, and dissimilarity measures are essential in clustering algorithms. Also, most...
