Search Results for author: Hang Hu

Found 7 papers, 1 papers with code

A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations

no code implementations13 Mar 2024 Hang Hu, Sidi Wu, Guoxiong Cai, Na Liu

In this work, a physics-driven GraphSAGE approach (PD-GraphSAGE) based on the Galerkin method and piecewise polynomial nodal basis functions is presented to solve computational problems governed by irregular PDEs and to develop parametric PDE surrogate models.

Transfer Learning

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images

no code implementations1 Nov 2023 Ni Yao, Hang Hu, Kaicong Chen, Chen Zhao, Yuan Guo, Boya Li, Jiaofen Nan, Yanting Li, Chuang Han, Fubao Zhu, Weihua Zhou, Li Tian

By using five-fold cross-validation, a deep learning model incorporating uncertainty estimation was developed to classify RCC subtypes into clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC).

Decision Making

CHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular Design

no code implementations21 Feb 2023 Mohammad Sajjad Ghaemi, Hang Hu, Anguang Hu, Hsu Kiang Ooi

The continuous property of the latent space, which characterizes the discrete chemical structures, provides a flexible representation for inverse design in order to discover novel molecules.

Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging

1 code implementation24 Oct 2022 David Helminiak, Hang Hu, Julia Laskin, Dong Hye Ye

Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions.


Training Overparametrized Neural Networks in Sublinear Time

no code implementations9 Aug 2022 Yichuan Deng, Hang Hu, Zhao Song, Omri Weinstein, Danyang Zhuo

The success of deep learning comes at a tremendous computational and energy cost, and the scalability of training massively overparametrized neural networks is becoming a real barrier to the progress of artificial intelligence (AI).

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