As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Abstract: For many years, topological data analysis (TDA) and deep learning (DL) have been considered separate data analysis and representation learning approaches, which have nothing in common. The ...

It is a central question in neuroscience to understand how different regions of the brain interact, how strongly they "talk" to each other. Researchers from the Max Planck Institute for Mathematics in ...

This repository contains the implementation of topological data analysis (TDA) methods for detecting adversarial examples in deep learning models, particularly focusing on Vision-Language models like ...

WASHINGTON, Nov 14 (Reuters) - The Commerce Department's Bureau of Economic Analysis said on Friday it was working to update its schedule of economic data releases affected by the recently ended ...

Researchers develop a novel topology-aware multiscale feature fusion network to enhance the accuracy and robustness of EEG-based motor imagery decoding Electroencephalography (EEG) is a fascinating ...

For a more detailed explanation for this package, this document will keep update for better understanding the source code. You can also try the playground I build to get familier with the algorithm ...

If you’d like an LLM to act more like a partner than a tool, Databot is an experimental alternative to querychat that also works in both R and Python. Databot is designed to analyze data you’ve ...

Image: Ralph Losey with AI. [EDRM Editor’s Note: EDRM is proud to publish Ralph Losey’s advocacy and analysis. The opinions and positions are Ralph Losey’s copyrighted work. All images in the article ...

Whenever we mull over what film to watch on Netflix, or deliberate between different products on an e-commerce platform, the gears of recommendation algorithms spin under the hood. These systems sort ...

QTSP, which is at the core of ranking algorithms, processes higher-order data. This data is represented as graphs and transformed into other graphs. This is usually taxing for classical computers, but ...