(deterministic) rate for all integrands in some function class. unlike sampling methods, deterministic quadratures can achieve higher-order convergence. they even overcome the curse of dimensionality, presuming
{\Delta }(n,T)}2^{-t|{\varvec{k}}|_1+r|{\varvec{k}}|_\infty }\lesssim \left\{ \begin{array}{ll} 2^{-\left( t-r-(tT-r)\frac{d-1}{d-T}\right) n}n^{d-1}, &{} {T\ge \frac{r}{t},} \\ 2^{-(t-r)n}, &{} {T<\frac{r}{t}} \end{array} \...
Hence, MOEA/PSL alleviates the curse of dimensionality and achieves a high search efficiency. In some other specifically designed MOEAs (e.g., MSKEA (Ding et al., 2022), SPS (Kropp et al., 2022), MDR-SAEA (Tan et al., 2021), MP-MMEA (Tian et al., 2021a), MGCEA (Tian et ...
On the other hand, the direct solving of the underlying PDE offers fast convergence and easy computation of the sensitivities, but the method is often prohibitively computationally demanding and suffers from the curse of dimensionality: standard discretization of the PDE leads to systems that grow ...
(either increased from 0 to 1, or left at 1). Thus, the SDRs of successive items of a sequence were “chained” together (as in Fig. 6). Also, in these experiments, the input level was a 10×10 binary pixel array and the objective was to store as many as possible 10-frame-long...
it is still fundamentally 2D—we can think of it as a 2D array of vectors, with each vector storing an RGB-color value. Model C’s initial convolutional layer consists of 96 convolutional filters of size 7×7, applied with stride 2. Each filter is therefore applied (224/2) 2 time...
Multipath propagation is both a curse and a blessing from a communications viewpoint [1]. On the one hand multipath propagation led to signalading fluctuationts onehan.,mulipth roagaio leads osgafdn- cutos in received signal strength-that severely impacts reliable communication. On the other ...
Our primary objective is to leverage a vast array of monthly macroeconomic variables to enhance the accuracy of forecasting quarterly Gross Domestic Product (GDP). To achieve this, we compared the following models: (1) The Autoregressive (AR) model, (2) The Mixed Data Sampling (MIDAS) model,...
Additionally, the results suggest that our proposed approach can cope efficiently with the “curse of dimensionality,” being capable of learning from small amounts of labeled data, and outperforming the state-of-the-art methods (ensembles of classifiers and feature selection methods) which are ...
methodology will allow us to achieve excellent results when dealing with high-dimensional spaces and sparse data. This technique is specially convenient because only sparse data is available when dealing with high-dimensional problems [12,19]. This allows us to cope with the curse of dimensionality...