Results 21 to 30 of about 6,652,811 (287)
Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment
Technologies, 2023 Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods
Gabriel Antonesi +3 more
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Learning to Learn Graph Topologies
, 2021 Learning a graph topology to reveal the underlying relationship between data entities plays an important role in various machine learning and data analysis tasks. Under the assumption that structured data vary smoothly over a graph, the problem can be formulated as a regularised convex optimisation over a positive semidefinite cone and solved by ...
Pu, X +4 more
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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
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Semi-decentralized Federated Ego Graph Learning for Recommendation [PDF]
The Web Conference, 2023 Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs.
Liang Qu +6 more
semanticscholar +1 more source
Learning Graph Augmentations to Learn Graph Representations
, 2022 Devising augmentations for graph contrastive learning is challenging due to their irregular structure, drastic distribution shifts, and nonequivalent feature spaces across datasets. We introduce LG2AR, Learning Graph Augmentations to Learn Graph Representations, which is an end-to-end automatic graph augmentation framework that helps encoders learn ...
Hassani, Kaveh, Khasahmadi, Amir Hosein
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Prediction of Synergistic Antibiotic Combinations by Graph Learning
Frontiers in Pharmacology, 2022 Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem.
Ji Lv +6 more
doaj +1 more source
CaT: Balanced Continual Graph Learning with Graph Condensation [PDF]
Industrial Conference on Data Mining, 2023 Continual graph learning (CGL) is purposed to continuously update a graph model with graph data being fed in a streaming manner. Since the model easily forgets previously learned knowledge when training with new-coming data, the catastrophic forgetting ...
Yilun Liu, Ruihong Qiu, Zi Huang
semanticscholar +1 more source
Remote Sensing, 2021 Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative ...
Guangyao Shi +3 more
doaj +1 more source
Hierarchical graph learning for protein–protein interaction
Nature Communications, 2023 Despite recent progress, machine learning methods remain inadequate in modeling the natural protein-protein interaction (PPI) hierarchy for PPI prediction.
Zi-Chao Gao +8 more
semanticscholar +1 more source
Permutation Equivariant Graph Framelets for Heterophilous Graph Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network (GNN) models and suggests aggregations beyond the one-hop neighborhood.
Jianfei Li +4 more
semanticscholar +1 more source