GitHub

Yingfangeng/aerofoil_surrogate_model

The goal of this repository is to outline a method of using graph neural networks and deep neural networks to predict the Lift and Drag of 2D Airfoils. Graph Neural Networks investigate the ...
GitHub

Graph neural network architecture for timeseries processing - PyTorch implementation

There was an error while loading. Please reload this page. This repository contains a Graph Neural Network (GNN) model designed for time series analysis. The model ...
EurekAlert!

Neural network helps design brand new proteins

WASHINGTON, August 29, 2023 – With their intricate arrangements and dynamic functionalities, proteins perform a plethora of biological tasks by employing unique arrangements of simple building blocks ...
IEEE

Few-shot graph learning with robust and energy-efficient memory-augmented graph neural network (MAGNN) based on homogeneous computing-in-memory

Abstract: Learning graph structured data from limited examples on-the-fly is a key challenge to smart edge devices. Here, we present the first chip-level demonstration of few-shot graph learning which ...
dbta

TigerGraph Unveils its ML Workbench to Deliver Deep Learning and Graph Neural Network Frameworks

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...
IEEE

Attention-aware heterogeneous graph neural network

Abstract: As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, ...
Óstáilte ar MSN

Backpropagation from Scratch in Python – Step by Step Neural Network Tutorial

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...