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Found 4 papers, 1 papers with code
Over-Squashing in Riemannian Graph Neural Networks
Most graph neural networks (GNNs) are prone to the phenomenon of over-squashing in which node features become insensitive to information from distant nodes in the graph.
AI-Assisted Discovery of Quantitative and Formal Models in Social Science
In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena.
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval
1) We present a novel differentially private method \textit{PrivateMail} for supervised manifold learning, the first of its kind to our knowledge.
Splintering with distributions: A stochastic decoy scheme for private computation
Performing computations while maintaining privacy is an important problem in todays distributed machine learning solutions.
