Search Results

• A flower cluster counting method based on density estimation and the density peak was carried out.
• The density distribution maps for apple flowers at tree-level was created.
• The potential for tree-level apple flower quantification ...

Accurate estimation of apple flower quantity is vital for flower thinning strategies, yield prediction, and other aspects related to orchard management. Compared to flower quantity estimation based on partial regions, achieving tree-level flower ...

Density estimation is crucial to statistical pattern recognition, in both the supervised and unsupervised frameworks. It is still an open problem, due to its intrinsic difficulties and to the many shortcomings of statistical parametric and non-...$$$$

We develop novel neural network-based implicit particle methods to compute high-dimensional Wasserstein-type gradient flows with linear and nonlinear mobility functions. The main idea is to use the Lagrangian formulation in the Jordan–...

• A neural network empowered implicit particle method is proposed for computing high dimensional nonlinear gradient flows.
• Our method leverages JKO's structure preservation and mesh-free discretization for high-dimensional problems.
• ...

Normalising flows are generative models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model. In the context of image ...

This paper focusses on the optimal implementation of a Mean Variance Estimation network (MVE network) (Nix and Weigend, 1994). This type of network is often used as a building block for uncertainty estimation methods in a regression setting, for ...

A new multivariate density estimator for stationary sequences is obtained by Fourier inversion of the thresholded empirical characteristic function. This estimator does not depend on the choice of parameters related to the smoothness of the ...

In this paper, a graph-based density clustering framework is proposed that detects the boundary points of clusters rather than cluster exemplars in high density regions. The framework introduces the connection density to measure the density ...

• Introducing the connection density to measure the density relationship between points.
• Utilizing the connectivity of the graph to achieve clustering by cutting off edges with low connection density.
• Automatically identifying ...

This paper proposes a novel algorithm for incremental learning over streaming data in a non-stationary environment. The idea refers to the applicability of Probabilistic Neural Networks (PNNs), commonly used as a fast and robust method for ...

• The incremental version of the Probabilistic Neural Network (IPNN) based on the orthogonal series, able to work in non-stationary environments.
• Mathematical proofs of the convergence of the proposed estimators for different types of ...

As an indispensable component of intelligent monitoring systems, crowd counting plays a crucial role in many fields, particularly crowd management and control during the COVID-19 pandemic. Despite the promising achievements of many methods, crowd ...

We study the asymptotic normality of the posterior distribution of canonical parameter in the exponential family under the Diaconis–Ylvisaker prior which is a conjugate prior when the dimension of parameter space increases with the sample size. ...

The class of recurrent mixture density networks is an important class of probabilistic models used extensively in sequence modeling and sequence-to-sequence mapping applications. In this class of models, the density of a target sequence in each ...

• Two aspects of RMDNs have been explored for efficient density estimation.
• A normalizing flow is employed to increase the flexibility of RMDNs.
• A parameter-sharing approach for GMM is applied that decomposes the precision matrix.

Crowd counting is one of the most complex research topics in the field of computer vision. There are many challenges associated with this task, including severe occlusion, scale variation, and complex background. Multi-column networks are commonly ...

Histograms are among the most popular methods used in exploratory analysis to summarize univariate distributions. In particular, irregular histograms are good non-parametric density estimators that require very few parameters: the number of bins with ...

Transportation of measure provides a versatile approach for modeling complex probability distributions, with applications in density estimation, Bayesian inference, generative modeling, and beyond. Monotone triangular transport maps—approximations ...

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into several scalar subproblems and then optimizes them cooperatively in their respective neighborhoods. Since the ...

Toroidal data is an extension of circular data on a torus and plays a critical part in various scientific fields. This article studies the density estimation of multivariate toroidal data based on semiparametric mixtures. One of the major ...

Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature, and computational ...${}_{\mathrm{}}$

• A Multi-Pathway Zooming Network is proposed, in which features at different resolutions are sequentially integrating and interacting during multi-glimpse ...

Crowd counting is a challenging task due to many challenges such as scale variations and noisy background. To handle these challenges, we propose a novel framework named Multi-Pathway Zooming Network (MZNet) in this paper. The proposed ...

The aim is twofold: (1) to indicate that the one-sample Wilcoxon signed rank test cannot be used directly when a center of symmetry is unknown; and (2) to propose and examine correct schemes for applying the Wilcoxon signed rank test ...

Nonparametric density estimation is studied for spherical data that may arise in many scientific and practical fields. In particular, nonparametric mixture models based on likelihood maximization are used. A nonparametric mixture has component ...