User profiles for "author:Si Si"
Si SiGoogle Research Verified email at google.com Cited by 3968 |
Effectiveness of general practice-based health checks: a systematic review and meta-analysis
S Si, JR Moss, TR Sullivan, SS Newton… - British Journal of General …, 2014 - bjgp.org
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 …
adults. Aim This review focuses on general practice-based health checks and their effects on …
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Graph convolutional network (GCN) has been successfully applied to many graph-based
applications; however, training a large-scale GCN remains challenging. Current SGD-based …
applications; however, training a large-scale GCN remains challenging. Current SGD-based …
Bregman divergence-based regularization for transfer subspace learning
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 …
problems, eg, the regularization of the norm in a reproducing kernel Hilbert space for the ill …
Scalable coordinate descent approaches to parallel matrix factorization for recommender systems
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 …
techniques for recommender systems. To handle web-scale datasets with millions of users …
Stable Large‐Area (10 × 10 cm2) Printable Mesoscopic Perovskite Module Exceeding 10% Efficiency
The commercial manufacturing of perovskite solar modules (PSM) suffers from stability
concerns and scalability issues. We demonstrate a hole‐conductor‐free printable solar …
concerns and scalability issues. We demonstrate a hole‐conductor‐free printable solar …
Scaling up dataset distillation to imagenet-1k with constant memory
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 …
smaller and highly informative synthetic ones to accelerate training and reduce storage …
DC-BENCH: Dataset condensation benchmark
Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that
captures the rich information encoded in the original dataset. As the size of datasets …
captures the rich information encoded in the original dataset. As the size of datasets …
A divide-and-conquer solver for kernel support vector machines
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 …
methods; however, the amount of computation required becomes the bottleneck when facing …
Memory efficient kernel approximation
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 …
computation issues in handling large kernel matrices, that are usually dense. Recently …
Parallel matrix factorization for recommender systems
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 …
techniques for recommender systems. To handle web-scale datasets with millions of users …