23 citations · 46 across the 9 of their papers we have counts for
9 papers
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Alan John Varghese, Aniruddha Bora, Mengjia Xu +1
Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender syst…
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks
Tomer Galanti, Mengjia Xu, Liane Galanti +1
In this paper, we investigate the Rademacher complexity of deep sparse neural networks, where each neuron receives a small number of inputs. We prove generalization bounds for mult…
Scalable algorithms for physics-informed neural and graph networks
Khemraj Shukla, Mengjia Xu, Nathaniel Trask +1
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale proc…
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
Mengjia Xu, Apoorva Vikram Singh, George Em Karniadakis
Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy.…
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images
Qian Zhang, Konstantina Sampani, Mengjia Xu +5
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. Adaptive opti…
Understanding graph embedding methods and their applications
Mengjia Xu
Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requ…
A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks
Mengjia Xu, David Lopez Sanz, Pilar Garces +3
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…
Multi-label Detection and Classification of Red Blood Cells in Microscopic Images
Wei Qiu, Jiaming Guo, Xiang Li +4
Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of sing…
Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net
Mo Zhang, Xiang Li, Mengjia Xu +1
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice. A major challenge for more robust segment…