marktechpost

A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a ...
XDA Developers

This self-hosted speed tracker monitors my network and graphs my internet's performance

Samir Makwana is a technology journalist and editor from India since past 18 years and his work appears on MakeUseOf, HowToGeek, GSMArena, BGR, GuidingTech, The Inquisitr, TechInAsia, TechWiser, and ...
science

Optical logic convolutional neural network

Optical computing presents a promising avenue to meet the escalating computational demands. However, optical analog computing is susceptible to environmental perturbations, relies heavily on ...
techxplore

New graph attention network models higher-order relationships in complex graph data

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 ...
KDnuggets

5 Breakthroughs in Graph Neural Networks to Watch in 2026

One of the most powerful and rapidly evolving paradigms in deep learning is graph neural networks (GNNs). Unlike other deep neural network architectures, such as feed-forward networks or convolutional ...
IEEE

Extractive Document Summarization with Graph Neural Networks and Topic Modeling in PyTorch

Abstract: Algorithmic text summarization task in natural language processing aims to represent a given text in a shorter and suitable form for a human reader by locating sentences of interest while ...
GitHub

Multi-View Conditional Information Bottleneck

Multi-View Conditional Information Bottleneck (MVCIB) is a novel architecture for pre-training Graph Neural Networks on 2D and 3D molecular structures and developed by NS Lab, CUK based on pure ...
marktechpost

A Coding Implementation to Build Neural Memory Agents with Differentiable Memory, Meta-Learning, and Experience Replay for Continual Adaptation in Dynamic Environments

In this tutorial, we explore how neural memory agents can learn continuously without forgetting past experiences. We design a memory-augmented neural network that integrates a Differentiable Neural ...
IEEE

PyTorch Ecosystem for Graph Neural Networks: A Comprehensive Review

Abstract: Deep learning has witnessed rapid progress through frameworks such as PyTorch, which has become the dominant choice for researchers and practitioners due to its dynamic computation, ...