… network with sparse links/edges spanning different misinformation topics across nine
categories. Nodes in the … Faloutsos, ”Using Ghost Edges for Classification in Sparsely Labeled …

… input data, such as edges and embosses, while … ghost-SE-ghost without shortcut connection
(denoted as NoSC, ie, we have removed only the shortcut connection only), the ghost–ghost …

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost
(SDRG) was proposed in this work for categorizing histopathology images of breast cancer…

… Using ghost edges for classification in sparsely labeled networks. In SIGKDD. 256–264. …
Since RS-GNN aims to densify the graphs to benefit predictions in sparsely labeled graphs, we …

… normalization to improve the image quality for breast cancer classification. Data augmentation
ran cutting-edge deep learning models and delivered promising outcomes by solving over…

… for transferring knowledge from a larger model … Ghost module to generate more features by
using fewer parameters. Specifically, an ordinary convolutional layer in deep neural networks …

… This paper provides an algorithm to design high-order distance metrics over a sparse
selection of features dedicated to classification. Our approach is based on Conditional Random …

… present GHOST, a database of 16,175 spectroscopically classified supernovae (SNe) and the
properties of their host galaxies. We have constructed GHOST using … Using dimensionality …

… Less layers and parameters, sparse connections, effective … for several lightweight networks,
including MobileNet [22], [24… a cheap ghost network (CGhostNet), which builds on the ghost …

… The missing neighborhood of edge pixels adopts zero-value padding. In the … Our
framework outperformed Ghost [62] in terms of classification performance and model efficiency …