A Novel Criterion for Classifiers Combination in Multistream Speech Recognition

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The finite element method is a well-established method for the numerical solution of partial differential equations (PDEs), both linear and nonlinear. However, the repeated re -assemblage of finite element matrices for nonlinear PDEs is frequently pointed ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Compound of the general formula (Ia), (Ib) and (Ic) R50 and R60 are different form each other and are selected from the group consisting of -R70, -ZR70, -Z-OH, -Z-NH2, -Z-SH, -Z-OC(O)R70, -OC(O)R70, - COOH and its corresponding salts, -C(O)NH2, -C(O)NH-R70 ...

One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) ...

The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpa ...

The next generation of high-energy physics (HEP) detectors will predominantly be silicon-based. As pixel sensor technology gains momentum and the number of channels surpasses one billion (for a volume of approximately 20 m3) to achieve the high resolution ...

Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomecha ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

A rank-adaptive integrator for the approximate solution of high-order tensor differential equations by tree tensor networks is proposed and analyzed. In a recursion from the leaves to the root, the integrator updates bases and then evolves connection tenso ...

A Novel Criterion for Classifiers Combination in Multistream Speech Recognition

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On the fast assemblage of finite element matrices with application to nonlinear heat transfer problems

Robust Collaborative Learning with Linear Gradient Overhead

Robust Outlier Rejection for 3D Registration with Variational Bayes

Biobased surfactants

Learning curves for the multi-class teacher-student perceptron

DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

Radiation-tolerant Multichannel Dew Point Temperature Monitoring System for High Energy Physics Applications

Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Distributionally Robust Linear Quadratic Control

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Rank-Adaptive Time Integration Of Tree Tensor Networks