Results 241 to 250 of about 260,346 (290)
AIChE Journal, EarlyView.Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim +2 more
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Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution
Advanced Intelligent Discovery, EarlyView.Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
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Complementary Kernel Density Estimation
Pattern Recognition Letters, 2012Generative models for vision and pattern recognition have been overshadowed in recent years by powerful non-parametric discriminative models. These discriminative models can learn arbitrary decision boundaries between classes and have proved very effective in classification and detection problems.
Xu Miao, Ali Rahimi, Rajesh P. N. Rao
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Reweighted kernel density estimation
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Martin L. Hazelton, Berwin A. Turlach
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On nonparametric kernel density estimates
Biometrika, 1990SUMMARY The paper introduces the idea of inadmissible kernels and shows that an Epanechnikov type kernel is the only admissible kernel. An analysis of kernel density estimates leads to two new methods of bias reduction. We also discuss a general method of improving kernel density estimates in the sense of having smaller mean squared error.
M. Samiuddin, G. M. El-Sayyad
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Data Structures in Kernel Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985We analyze and compare several data structures and algorithms for evaluating the kernel density estimate. Frequent evaluations of this estimate are for example needed for plotting, error estimation, Monte Carlo estimation of probabilities and functionals, and pattern classification. An experimental comparison is included.
Luc Devroye, Fred Machell
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Estimating the Variance of a Kernel Density Estimation
2010This article proposes an interval-valued extension of kernel density estimation. We show that the imprecision of this interval-valued estimation is highly correlated with the variance of the density estimation induced by the statistical variations of the set of observations.
Bilal Nehme +2 more
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VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES
Australian Journal of Statistics, 1990SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
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Generalized Kernel Density Estimator
Theory of Probability & Its Applications, 2000Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
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