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GitHub14d
Self-supervised learning of sea surface temperature patterns using a convolutional autoencoder and UMAP-based clustering - GitHub
This project applies a self-supervised deep learning pipeline to daily NOAA OISST (Optimum Interpolation Sea Surface Temperature) data. Using a convolutional autoencoder, it extracts latent ...
GitHub1mon
A PyTorch implementation of a Variational Autoencoder trained on the MNIST dataset. The project demonstrates digit generation from random latent vectors and reconstruction of ...
This project implements a Variational Autoencoder (VAE) in PyTorch, trained on the MNIST dataset. It features a Gradio web app for generating new digits and reconstructing uploaded digit images.
IEEE1mon
A Surrogate-Assisted High-Dimensional Mixed-Variable Evolutionary Framework and Its Application to Vehicle Lightweighting Design | IEEE Journals & Magazine | IEEE Xplore
In SHIME, we develop an autoencoding Bayesian neural network (ABNN), which is a dimensionality-reduction-based end-to-end surrogate model. ABNN consists of an autoencoder branch and a Bayesian network ...
VoxelMatters5d
Sparc3D introduces next-gen high-resolution 3D model generation
Explore how Sparc3D transforms 2D images into detailed 3D models with AI-powered efficiency and precision. Discover more.
IEEE22d
Jia-Hao Xu | IEEE Xplore Author Details
Affiliations: [School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China].
Frontiers1mon
Erratum: Graph-informed convolutional autoencoder to classify brain responses during sleep - Frontiers
Citation: Frontiers Production Office (2025) Erratum: Graph-informed convolutional autoencoder to classify brain responses during sleep. Front. Neurosci. 19:1627975. doi: 10.3389/fnins.2025.1627975 ...