Results 21 to 30 of about 21,280,601 (295)
Amortized Inference for Causal Structure Learning [PDF]
Neural Information Processing Systems, 2022 Inferring causal structure poses a combinatorial search problem that typically involves evaluating structures with a score or independence test. The resulting search is costly, and designing suitable scores or tests that capture prior knowledge is ...
Lars Lorch +4 more
semanticscholar +1 more source
RDGSL: Dynamic Graph Representation Learning with Structure Learning [PDF]
International Conference on Information and Knowledge Management, 2023 Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise.
Siwei Zhang +6 more
semanticscholar +1 more source
Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network
IEEE Access, 2021 Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases.
Luxuan Qu +6 more
doaj +1 more source
Graph Structure Learning with Variational Information Bottleneck [PDF]
AAAI Conference on Artificial Intelligence, 2021 Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming the observed structure perfectly depicts the accurate and complete relations
Qingyun Sun +6 more
semanticscholar +1 more source
Homophily-enhanced Structure Learning for Graph Clustering [PDF]
International Conference on Information and Knowledge Management, 2023 Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existing GNN-based graph clustering methods, they often overlook the quality of ...
Ming Gu +7 more
semanticscholar +1 more source
Heterogeneous Graph Structure Learning for Graph Neural Networks
AAAI Conference on Artificial Intelligence, 2021 Heterogeneous Graph Neural Networks (HGNNs) have drawn increasing attention in recent years and achieved outstanding performance in many tasks. The success of the existing HGNNs relies on one fundamental assumption, i.e., the original heterogeneous graph
Jianan Zhao +5 more
semanticscholar +1 more source
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery [PDF]
ACM Computing Surveys, 2021 Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery.
M. Vowels, N. C. Camgoz, R. Bowden
semanticscholar +1 more source
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting [PDF]
International Joint Conference on Artificial Intelligence, 2022 Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously.
Hongyuan Yu +6 more
semanticscholar +1 more source
Entropy, 2020 Score-based algorithms that learn Bayesian Network (BN) structures provide solutions ranging from different levels of approximate learning to exact learning.
Zhigao Guo, Anthony C. Constantinou
doaj +1 more source
Compact Graph Structure Learning via Mutual Information Compression [PDF]
The Web Conference, 2022 Graph Structure Learning (GSL) recently has attracted considerable attentions in its capacity of optimizing graph structure as well as learning suitable parameters of Graph Neural Networks (GNNs) simultaneously.
Nian Liu +5 more
semanticscholar +1 more source