Gaussian function ≈ Gaussian function
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Gaussian Error Linear Units (GELUs) Open
We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $xΦ(x)$, where $Φ(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity wei…
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Generalized Correntropy for RobustAdaptive Filtering Open
As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successf…
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Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries Open
Battery calendar aging prediction is of extreme importance for developing durable electric vehicles. This paper derives machine learning-enabled calendar aging prediction for lithium-ion batteries. Specifically, the Gaussian process regres…
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FCHL revisited: Faster and more accurate quantum machine learning Open
We introduce the FCHL19 representation for atomic environments in molecules or condensed-phase systems. Machine learning models based on FCHL19 are able to yield predictions of atomic forces and energies of query compounds with chemical ac…
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Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models Open
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propos…
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Asymptotic Laws for the Spatial Distribution and the Number of Connected Components of Zero Sets of Gaussian Random Functions Open
We study the asymptotic laws for the spatial distribution and the number of connected components of zero sets of smooth Gaussian random functions of several real variables.The primary examples are various Gaussian ensembles of real-valued …
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To understand deep learning we need to understand kernel learning Open
Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data exactly. Despite this "overfitting", they perform well on te…
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The Impact of Different Kernel Functions on the Performance of Scintillation Detection Based on Support Vector Machines Open
Scintillation caused by the electron density irregularities in the ionospheric plasma leads to rapid fluctuations in the amplitude and phase of the Global Navigation Satellite Systems (GNSS) signals. Ionospheric scintillation severely degr…
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An alternative form of the super-Gaussian wind turbine wake model Open
A new analytical wind turbine wake model, based on a super-Gaussian shape function, is presented. The super-Gaussian function evolves from a nearly top-hat shape in the near wake to a Gaussian shape in the far wake, which is consistent wit…
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Gaussian and plane-wave mixed density fitting for periodic systems Open
We introduce a mixed density fitting scheme that uses both a Gaussian and a plane-wave fitting basis to accurately evaluate electron repulsion integrals in crystalline systems. We use this scheme to enable efficient all-electron Gaussian b…
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Gaussian Process Regression for Transition State Search Open
We implemented a gradient-based algorithm for transition state search which uses Gaussian process regression. Besides a description of the algorithm, we provide a method to find the starting point for the optimization if only the reactant …
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Performance Investigation of Principal Component Analysis for Intrusion Detection System Using Different Support Vector Machine Kernels Open
The growing number of security threats has prompted the use of a variety of security techniques. The most common security tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine …
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Gaussian Process Behaviour in Wide Deep Neural Networks Open
Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between random, wide, fully connected, feedforward net…
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Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set Open
Fast and accurate detection of ripe tomatoes on plant, which replaces manual labor with a robotic vision-based harvesting system, is a challenging task. Tomatoes in adjacent positions are easily mistaken as a single tomato by image recogni…
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Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine Open
Online state-of-health (SOH) estimation is critical for second-use retired lithium-ion batteries. However, the SOH of retired batteries is highly nonlinear, and the existing degradation trend data are limited. Consequently, achieving accur…
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Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques Open
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dyna…
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Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials Open
Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, …
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Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising Open
The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms. In this paper we revisit the Gaussian mechanism and show that the original analysis has several important limitatio…
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The SPDE approach for Gaussian and non-Gaussian fields: 10 years and\n still running Open
Gaussian processes and random fields have a long history, covering multiple\napproaches to representing spatial and spatio-temporal dependence structures,\nsuch as covariance functions, spectral representations, reproducing kernel\nHilbert…
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An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection Open
Sepsis is a poorly understood and potentially life-threatening complication that can occur as a result of infection. Early detection and treatment improves patient outcomes, and as such it poses an important challenge in medicine. In this …
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Fast Incremental SVDD Learning Algorithm with the Gaussian Kernel Open
Support vector data description (SVDD) is a machine learning technique that is used for single-class classification and outlier detection. The idea of SVDD is to find a set of support vectors that defines a boundary around data. When deali…
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Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction Open
Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the histor…
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Application of Machine Learning to a Medium Gaussian Support Vector Machine in the Diagnosis of Motor Bearing Faults Open
In recent years, artificial intelligence technology has been widely used in fault prediction and health management (PHM). The machine learning algorithm is widely used in the condition monitoring of rotating machines, and normal and fault …
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Reconstructing QCD spectral functions with Gaussian processes Open
We reconstruct ghost and gluon spectral functions in 2+1 flavor QCD with Gaussian process regression. This framework allows us to largely suppress spurious oscillations and other common reconstruction artifacts by specifying generic magnit…
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Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability Open
In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, …
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How to quantify a distance‐dependent landscape effect on a biological response Open
To quantify the effect of the surrounding landscape context on a biological response at a site, most studies measure landscape variables within discs centred on this biological response (threshold‐based method, TBM). This implicitly assume…
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Spatio-Temporal Gaussian Process Models for Extended and Group Object Tracking With Irregular Shapes Open
| openaire: EC/H2020/688082/EU//SETA
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How big is an OMI pixel? Open
The Ozone Monitoring Instrument (OMI) is a push-broom imaging spectrometer, observing solar radiation backscattered by the Earth's atmosphere and surface. The incoming radiation is detected using a static imaging CCD (charge-coupled device…
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A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance Open
Forest leaf chlorophyll (Cab) and carotenoid (Cxc) are key functional indicators for the state of the forest ecosystem. Current machine learning models based on hyperspectral reflectance are widely applied to estimate leaf Cab and Cxc cont…
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Brief communication: A double-Gaussian wake model Open
In this paper, an analytical wake model with a double-Gaussian velocity distribution is presented, improving on a similar formulation by Keane et al. (2016). The choice of a double-Gaussian shape function is motivated by the behavior of th…