Results 41 to 50 of about 229,000 (311)
Companion Proceedings of the Web Conference 2022, 2022 Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear.
Shuo Yu 0001 +3 more
openaire +2 more sources
ESANN 2021 proceedings, 2021 Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators.
Bacciu, Davide +3 more
openaire +3 more sources
Graph-Based Semi-Supervised Learning as a Generative Model [PDF]
, 2018 This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional ...
Yan Liu (5411249) +2 more
core +3 more sources
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning [PDF]
, 2022 Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering.
Jin, D +13 more
core +1 more source
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Accepted to IEEE Conference on Computer Vision and Pattern Recognition (CVPR ...
Chen Wang 0033 +3 more
openaire +2 more sources
Prototypical Graph Contrastive Learning [PDF]
, 2022 Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules. But in practice, precise graph annotations are generally very expensive and time-consuming.
Zhao, Ruihui +19 more
core +2 more sources
Joint Graph-Sequence Learning for Molecular Property Prediction [PDF]
, 2022 Molecular property prediction has achieved promising improvement for accelerating drug development with machine learning models. The emergence of graph neural networks especially benefits the discriminative representation learning of molecular graph data,
Uddamvathanak, R, Zheng, X, Pan, S
core +1 more source
Privatized Graph Federated Learning
EURASIP Journal on Advances in Signal Processing, 2023 Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting
Elsa Rizk, Stefan Vlaski, Ali H. Sayed
openaire +4 more sources
Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization
Frontiers in Big DataThe emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines.
Revathy Venkataramanan +11 more
doaj +1 more source