Scientific Research Publishing

KAN-Transformer Fusion with Mixture of Experts for Temporal Imputation of Spatiotemporal Air Pollution Data ()

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
IEEE

Event Camera Object Detection Using Bayesian Neural Network-Conditional Variational Autoencoders and Improved YOLOv10

Abstract: We conducted an in-depth investigation into the impact of Conditional Variational Autoencoders (CVAE) and Bayesian Neural Networks (BNN) on high dynamic range (HDR) image reconstruction. A ...
Live Science

'Thermodynamic computer' can mimic AI neural networks — using orders of magnitude less energy to generate images

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require. When you purchase through links on our site, we may earn an affiliate ...
IEEE

CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder With Input Convex Neural Networks

Abstract: In neural architecture search (NAS) methods based on latent space optimization (LSO), a deep generative model is trained to embed discrete neural architectures into a continuous latent space ...
GitHub

Deep neural networks

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...
Ars Technica

Researchers isolate memorization from problem-solving in AI neural networks

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...
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

PyTorch Matrix Product Operators

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 ...
Scientific Research Publishing

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J. et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...