Semiconductor Engineering

Hypergraph-based Techniques To Map Spiking Neural Networks on Neuromorphic HW (Politecnico di Milano)

A new technical paper titled “A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware” was published by ...
AlphaGalileo

A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in ...
Engadget

Apple's M5 chip pushes AI performance with new 'Neural Accelerators'

Like clockwork, Apple has introduced a new M-series chip on updated versions of the iPad Pro, MacBook Pro and for the first time, Apple Vision Pro. The new M5 chip shares plenty of similarities with ...
IEEE

Multi-Agent Deep Reinforcement Learning for Dynamic Routing in MANETs Using Graph Neural Networks

Abstract: Mobile Ad Hoc Networks (MANETs) consist of decentralized wireless networks with dynamic topologies and frequent link failures which make achieving efficient routing extremely difficult.
Frontiers

DGAT: a dynamic graph attention neural network framework for EEG emotion recognition

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
The Conversation

MIT researchers say using ChatGPT can rot your brain. The truth is a little more complicated

Associate Professor and Associate Director of the Centre for Change and Complexity in Learning (C3L), Education Futures, University of South Australia Since ChatGPT appeared almost three years ago, ...
IEEE

Wasserstein Graph Neural Networks for Graphs With Missing Attributes

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
SiliconANGLE

Neo4j goes serverless, bringing graph analytics to any data source

Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications. Most ...