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Found 7 papers, 1 papers with code
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations
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.
A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images
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).
CHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular Design
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.
Machine learning for the prediction of safe and biologically active organophosphorus molecules
Drug discovery is a complex process with a large molecular space to be considered.
Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging
Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions.
Training Overparametrized Neural Networks in Sublinear Time
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).
Sublinear Time Algorithm for Online Weighted Bipartite Matching
Online bipartite matching is a fundamental problem in online algorithms.
