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, ...
IEEE

Scaling Graph Neural Networks for Particle Track Reconstruction

Abstract: Particle track reconstruction is an important problem in high-energy physics (HEP), necessary to study properties of subatomic particles. Traditional track reconstruction algorithms scale ...
Hosted on MSN

Network in Network (NiN) Explained – Deep Neural Network Tutorial with PyTorch

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
blockchain

PyTorch for Deep Learning Professional Certificate: Definitive AI Training Program for Building Modern Neural Networks

According to Andrew Ng (@AndrewYNg), DeepLearning.AI has launched the PyTorch for Deep Learning Professional Certificate taught by Laurence Moroney (@lmoroney). This three-course program covers core ...
GitHub

[BUG] - Minor: incorrect description in the Build the Neural Network tutorial

Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
GitHub

Pruning Convolutional Neural Networks

This repository explores what pruning of neural networks is and how to apply it using pyTorch. For this purpose a very small ResNet10 has been chosen, trained with FashionMNIST. In unstructured ...