Deep Graph Laplacian Regularization for Robust Denoising of Real Images Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung CVPR 2019 Learning Context Graph for Person Search Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang CVPR 2019 Graphonomy: Universal Human Parsing v...
[17] R. Kim, C. H. So, M. Jeong, S. Lee, J. Kim, and J. Kang. Hats: A hierarchical graph attention network for stock movement prediction, 2019. [18] T. N. Kipf and M. Welling. Variational graph auto-encoders. NIPS Workshop on Bayesian Deep Learning, 2016. [19] T. N. K...
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds Lasse Hansen, Jasper Diesel, Mattias P. Heinrich ECCV 2018 Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network Feng Mao, Xiang Wu, Hui Xue, Rong Zhang ECCV 2018 Graph R-CNN for Scene Graph Gen...
where taking the neighborhood of each pixel into account is critical for the performance of downstream tasks, we introduced a graph convolutional autoencoder that integrates both the gene expression of a cell and that of its neighbors. Our graph-based autoencoder structure decodes both a cell’s...
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters Asiri Wijesinghe, Qing Wang NeurIPS 2019 Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology Nima Dehmamy, Albert-László Barabási, Rose Yu NeurIPS 2019 A Flexible Generative Framework for Graph-...
denoising autoencoder. This involves the addition of Gaussian noise to the similarity data and utilizing a graph variational autoencoder to reconstruct signed associations, thereby overcoming biases present in the similarity data. For convenience, the node similarity features were represented asF, as ...
Graph Representation Learning (Graph Neural Networks, GNN) A Review of methods and applications, Zhou Jie 2020, on AI Open Figure. An overwiew of comp
Denoising process The core principle of the PCA-based framework for denoising lies in the observation that noise typically resides in the lower eigenvalues, while the signal is concentrated in the higher eigenvalues. In the context of GraphPCA, the incorporation of spatial information allows for an...
A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep autoencoders. Mech. Syst. Signal Process. 102, 278–297 (2018). Article ADS Google Scholar Haidong, S. et al. Intelligent fault diagnosis of rolling bearing using deep wavelet autoencoder with extreme learnin...
LTMG can translate the input gene expression data into a discretized regulatory signal as the regularizer for the feature autoencoder. The feature autoencoder learns a dimensional representation of the input as embedding, upon which a cell graph is constructed and pruned. The graph autoencoder ...