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Found 9 papers, 2 papers with code
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics.
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks
In this paper, we investigate the Rademacher complexity of deep sparse neural networks, where each neuron receives a small number of inputs.
Scalable algorithms for physics-informed neural and graph networks
For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs).
BIG-bench Machine Learning
Physics-informed machine learning
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
However, recent advances mostly focus on learning node embeddings as deterministic "vectors" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node embedding in the latent space.
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images
Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness.
Understanding graph embedding methods and their applications
Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks.
A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms.
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images
As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.
