Background A recent review concluded that general health checks fail to reduce mortality in
adults. Aim This review focuses on general practice-based health checks and their effects on …

Graph convolutional network (GCN) has been successfully applied to many graph-based
applications; however, training a large-scale GCN remains challenging. Current SGD-based …

The regularization principals [31] lead approximation schemes to deal with various learning
problems, eg, the regularization of the norm in a reproducing kernel Hilbert space for the ill-…

Dataset Distillation is a newly emerging area that aims to distill large datasets into much
smaller and highly informative synthetic ones to accelerate training and reduce storage. Among …

The commercial manufacturing of perovskite solar modules (PSM) suffers from stability
concerns and scalability issues. We demonstrate a hole‐conductor‐free printable solar module …

Scaling kernel machines to massive data sets is a major challenge due to storage and
computation issues in handling large kernel matrices, that are usually dense. Recently, many …

Matrix factorization, when the matrix has missing values, has become one of the leading
techniques for recommender systems. To handle web-scale datasets with millions of users and …

… The Si-Si double-bond character of 3 is further supported by the π Si=Si –π* Si=Si absorption
(λ max = 466 nm, in THF), which is comparable to the reported ultraviolet/visible absorption …

The kernel support vector machine (SVM) is one of the most widely used classification
methods; however, the amount of computation required becomes the bottleneck when facing …

In this paper, we study the gradient boosted decision trees (GBDT) when the output space is
high dimensional and sparse. For example, in multilabel classification, the output space is a …