Spread the loveFinding the extrema of a multivariable function is an essential skill in calculus and applied mathematics. This process allows you to determine the minima and maxima of a function ...

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex ...

Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are widely applied ...

In nonparametric multivariate regression analysis, we seek methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. The original ...

Finally, we note that these approximation results for functions of bounded variation are also valid for Fourier sums and the corresponding multivariate hyperbolic cross-variants, where the results can ...

Book Chapter: Heal M & Li J (2020) Finding the Maximal Independent Sets of a Graph Including the Maximum Using a Multivariable Continuous Polynomial Objective Optimization Formulation. In: Arai K, ...

One of the challenges with emulating the response of a multivariate function to its inputs is the quantity of data that must be assimilated, which is the product of the number of model evaluations and ...

We compared our new algorithm, called General Value Function Outlier Detection (GVFOD), to existing methods for outlier detection in multivariate data. As GVFOD uses reinforcement learning methods, we ...

mvsp is a Python implementation of the protocols presented in Quantum state preparation for multivariate functions. The protocols are based on function approximations with finite Fourier or Chebyshev ...

[2] On shrinkage estimation for balanced loss functions. Journal of Multivariate Analysis (2020). [3] Bayes minimax estimation of the multivariate normal mean vector under balanced loss function.