In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global ...
Self- organizing map (SOM) is an unsupervised neural network technique capable of analyzing large and complex multivariate data. It attempts to address the problems of high- dimensional data and identify the underlying patterns by reducing the dimensionality achieved through grouping of similar object...
传统的进化算法只能够针对一个目标或者是一小部分目标,比如,优化图像去匹配ImageNet的一个类。在这里,我们使用了一个新的算法,叫做:multi-dimensional archive of phenotypic elites MAP-Elites,这个算法可以让我们同时的进化出一个群体,如:ImageNet的1000个类。 MAP-Elites的工作方式:在每一轮迭代中,随机选择一个样...
Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary computations downstream and inflates the time required...
High strain rate stress-strain data can be generated by measuring the strains in the incident and transmitted bars with the help of strain gages by using one-dimensional wave propagation analysis. Download: Download full-size image Fig. 1. Schematic diagram of split Hopkinson pressure bar (SHPB...
Finally, a deep neural network based on the adversarial autoencoder is proposed as an effective framework for learning the latent low-dimensional space of nonlinear sufficient dimension reduction. AAE-SDR neural network-based high-dimensional reliability analysis This work presents a high-dimensional ...
"High-Dimensional Dense Residual Convolutional Neural Network for Light Field Reconstruction"IEEE Transactions on Pattern Analysis and Machine Intelligence,Nan Meng, Hayden K-H. So, Xing Sun, Edmund Y. Lam, 2019.[Paper] "High-order Residual Network for Light Field Super-Resolution"The 34th AAAI ...
We demonstrate its efficacy to parameterize potential models (physics based and high-dimensional neural networks) for 54 different elemental systems across the periodic table as well as alloys. We analyze error trends across different elements in the latent space and trace their origin to elemental ...
interferometric method to analyze the polarizations based on a tri-channel chiral metasurface. A deep convolutional neural network is also incorporated to enable fast, robust and accurate polarimetry. Spatially uniform and nonuniform polarizations are both measured through the metasurface experimentally. Di...
To aid researchers and practitioners working on or whose work depends on the problem, we setup this benchmark for Nearest Neighbor Search (NNS) based on the Euclidean distance on High Dimensional Data. The benefit is twofold: For researchers, it allows one toeasilycompare their new algorithms wi...