1 code implementation • 5 Jul 2023 • Alan John Varghese, Aniruddha Bora, Mengjia Xu, George Em Karniadakis
Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics.
no code implementations • 28 Jan 2023 • Tomer Galanti, Mengjia Xu, Liane Galanti, Tomaso Poggio
In this paper, we investigate the Rademacher complexity of deep sparse neural networks, where each neuron receives a small number of inputs.
no code implementations • 16 May 2022 • Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis
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
1 code implementation • 28 Sep 2021 • Mengjia Xu, Apoorva Vikram Singh, George Em Karniadakis
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.
no code implementations • 5 Jun 2021 • Qian Zhang, Konstantina Sampani, Mengjia Xu, Shengze Cai, Yixiang Deng, He Li, Jennifer K. Sun, George Em Karniadakis
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.
no code implementations • 15 Dec 2020 • 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 requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks.
no code implementations • 8 May 2020 • Mengjia Xu, David Lopez Sanz, Pilar Garces, Fernando Maestu, Quanzheng Li, Dimitrios Pantazis
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.
no code implementations • 7 Oct 2019 • Wei Qiu, Jiaming Guo, Xiang Li, Mengjia Xu, Mo Zhang, Ning Guo, Quanzheng Li
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.
no code implementations • 23 Oct 2017 • Mo Zhang, Xiang Li, Mengjia Xu, Quanzheng Li
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.