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Found 7 papers, 1 papers with code
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models.
A coupled-mechanisms modelling framework for neurodegeneration
We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
We further propose Graph selective attention networks (SATs) to learn representations from the highly correlated node features identified and investigated by different SA mechanisms.
Exploring Linear Feature Disentanglement For Neural Networks
Non-linear activation functions, e. g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs).
Learning Conjoint Attentions for Graph Neural Nets
Given the novel Conjoint Attention strategies, we then propose Graph conjoint attention networks (CATs) that can learn representations embedded with significant latent features deemed by the Conjoint Attentions.
Graph Joint Attention Networks
In this paper, we propose Graph Joint Attention Networks (JATs) to address the aforementioned challenge.
A Multi-Task Gradient Descent Method for Multi-Label Learning
Multi-label learning studies the problem where an instance is associated with a set of labels.
