A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive processors” was published by researchers at UCLA. “Nonlinear computation is ...

Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs large-scale nonlinear computations using linear materials. Reported in ...

This project is a Retrieval-Augmented Generation (RAG) system implementing the SemRAG architecture as described in the original paper. This system processes a provided PDF into semantic chunks, ...

Abstract: We present SNAP (Small-world Network Analysis and Partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability ...

This is the official implementation of ParaParallelizing Node-Level Explainability in Graph Neural Networks. In this README you will find as much information as possible so you be able to replicate ...

Abstract: The Graph Isomorphism (GI) problem has been extensively studied due to its significant applications. The most effective class of GI algorithms, i.e., canonical labeling algorithms, are ...