Mapping cell development with mathematics-informed machine learning

The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
IEEE

Nonlinear Subspace Clustering via Adaptive Graph Regularized Autoencoder

Abstract: Most existing subspace clustering methods focus on learning a meaningful (e.g., sparse or low-rank) representation of the data. However, they have the following two problems which greatly ...
IEEE

A Parallel Nonlinear Factor Recovery Method for VIO Based on a Factor Graph

Abstract: Under factor graph-based visual-inertial odometry (VIO), is usually used to limit the continuously increasing numbers of nodes (states) and edges (observations). However, this approach will ...
GitHub

Refined Graph Convolution Collaborative Filtering with Concise and Expressive Embedding

Graph Convolution Networks(GCNs) have attracted significant attention and have become the most popular method for learning graph representations. In recent years, many efforts have focused on ...
The New York Times

What's Going On in This Graph?

Where are insurance companies dropping homeowners? By The Learning Network How does life expectancy vary by state and country? By The Learning Network A new collection of graphs, maps and charts ...
GitHub

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.

GCA-ROM requires pytorch, pyg, matplotlib, scipy and h5py. They can be easily installed via pip or conda. In the notebook folder, one can find the *.ipynb files ...
Frontiers

LG-H-PPO: offline hierarchical PPO for robot path planning on a latent graph

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...