We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes...
First, MBTCN divides the overall variables into several sub-blocks based on process mechanisms and uses one-dimensional convolutional neural network (1D-CNN) architectures to extract temporal-correlated features in each sub-block, with the 1D-CNN network sliding over time steps. Thus, the adjacent...
We show in this work that non-local correlations of dihedral potentials play a decisive role in the description of the total molecular energy—an effect which is neglected in most state-of-the-art dihedral force fields. We furthermore present an efficient machine learning approach to compute ...
We describe some general features in the transient behaviour of strongly correlated transition metal oxides, following ultrafast excitation by a femtosecond laser pulse. Our analysis is based on time-resolved reflectivity measurements on... B.,Mansart,D.,... - 《Journal of Modern Optics》 被引量...
FashionMNIST following Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. See datasets/fashionmnist.py. Note that every dataset implementation comes with its own download functionality, so no need to download and setup each dataset externally! Additional Features Beyond ke...
If you have preexisting features in your dataframe (regardless if you use TuneTA to create new ones), I've added a helper prune_df function to prune the all of the features based on intercorrelation. This is helpful, for example, if you have custom features that you would like to combi...
In this study, the authors focus on blind digital modulation identification in the spatially correlated MIMO system and deliver a robust signal recognition algorithm based on extreme learning machine (ELM) and higher order statistical features for MIMO signal identification without a priori knowledge of...
Machine learning for molecular dynamics with strongly correlated electronsWe use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion U. ...
Design, Setting, and Participants In this population-based cross-sectional study, unsupervised machine learning was used to identify themes and topics in online discussions generated between January 1, 2019, and May 20, 2021, by Chinese users of mHealth apps for weight loss. Main Outcomes and Me...
For example, machine learning algorithms may be tailored in attempts to realize an efficient use of server resources for predictive maintenance for the associated servers. Accordingly, machine learning may be used to improve the useful life of server infrastructure by reducing inefficient use of ...