Therefore, in this paper, we propose a novel multimodal data fusion method, named IG-GRD, based on graph representation learning for imaging and genetic data. Firstly, we construct imaging graphs and genetic graphs based on the characteristics of fMRI and SNP data, mapping the data from these ...
Specifically, we design a graph contrastive learning strategy, significantly reducing the requirement of labeled data. We disentangle attention and redundant graph representations to extract intrinsic features and exclude the influence of redundant information. In addition, we utilize the disentangled two ...
natural-language-processingdeep-learningneural-networkstyle-transferdisentangled-representations UpdatedOct 18, 2019 Python xiangwang1223/disentangled_graph_collaborative_filtering Star138 Disentagnled Graph Collaborative Filtering, SIGIR2020 collaborative-filteringrecommender-systemrecommendationdistance-correlationdisentangl...
We show via a causal perspective, the benefits of learning disentangled seasonal-trend representations for time series forecasting via contrastive learning. We propose CoST, a time series representation learning approach which leverages inductivebiasesin the model architecture to learn disentangled seasonal a...
2019 TCSVT之ReID:SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-id,程序员大本营,技术文章内容聚合第一站。
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning Then we train two models via supervised objective and mutual information (MI)-based constraints respectively. To ensure the meaningful transfer of knowledge ... Y Wang,X Luo,C Chen,... - 《IEEE Transactions on ...
While these methods mainly focus on learning the embedding representation of FC, ignoring its geometry. More recently, graph convolutional network (GCN), a powerful deep representation learning method for graph-structured data, has achieved considerable attention in the field of FC analysis (Ktena et...
Given the societal relevance of machine-learning driven decisionprocesses, fairness has become a highly active f ield [ 4 ]. Assuming the existence of a complex causalgraph with partially observed and potentially confounded observations [ 48 ], sensitive protectedattributes (e.g. gender, race, etc...
(1) a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent representations, (2) a Generative Adversarial Network (GAN) that translates the latent representations of expressive faces into those of neutral faces, (3) and an identity recognition sub-network taking advantage of ...
Paper tables with annotated results for DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning