Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

In this paper, we propose PCPNet, a deep‐learning based approach for estimating local 3D
shape properties in point clouds. In contrast to the majority of prior techniques that …

Graph representations are often used to model structured data at an individual or population
level and have numerous applications in pattern recognition problems. In the field of …

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas
chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable …

While tumor dynamic modeling has been widely applied to support the development of
oncology drugs, there remains a need to increase predictivity, enable personalized therapy …

We propose a new algorithm for converting unstructured triangle meshes into ones with a
consistent topology for machine learning applications. We combine the orthogonal depth …

Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …

Directly learning features from the point cloud has become an active research direction in
3D understanding. Existing learning-based methods usually construct local regions from the …

We introduce a novel learning-based method to recover shapes from their Laplacian
spectra, based on establishing and exploring connections in a learned latent space. The …

Graphs frequently appear as a type of data structure that efficiently models a set of
interrelated objects as nodes and their relations as edges between them. Geometric deep …