Abstract: We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (nonnegativity-constrained autoencoder), that learns features that show part-based ...
For three decades, functional neuroimaging (fMRI) has been shaping the understanding of the human brain. A major obstacle for ...
Fluid–structure interaction (FSI) governs how flowing water and air interact with marine structures—from wind turbines to ...
Abstract: Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks.
ABSTRACT: Artificial intelligence is reshaping the field of financial risk control, bringing revolutionary changes to risk management. This study systematically explores the application prospects and ...
Autoencoders are a class of neural networks that aim to learn efficient representations of input data by encoding and then reconstructing it. They comprise two main parts: the encoder, which ...
Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data. They are similar to regular autoencoders, which consist of an encoder and decoder. The encoder takes ...
This repository provides the official implementation of Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders at AAAI 2024. Learning 3D representation plays a critical ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback