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, ...

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 ...

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 ...

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 ...

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 ...

This library provides PyTorch implementations of tensor-train decomposed neural network layers that can significantly reduce the number of parameters in deep neural networks while maintaining accuracy ...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...