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Found 11 papers, 1 papers with code
Transformer with Syntactic Position Encoding for Machine Translation
It has been widely recognized that syntax information can help end-to-end neural machine translation (NMT) systems to achieve better translation.
Incorporating Riemannian Geometric Features for Learning Coefficient of Pressure Distributions on Airplane Wings
The aerodynamic coefficients of aircrafts are significantly impacted by its geometry, especially when the angle of attack (AoA) is large.
Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures
As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data.
Novel deep learning methods for 3D flow field segmentation and classification
Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow.
Break The Spell Of Total Correlation In betaTCVAE
The novel model enables VAE to adjust the parameter capacity to divide dependent and independent data features flexibly.
A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method
Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction.
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years.
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans
To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models without leakage of data privacy.
Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain
Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform.
Flow Field Reconstructions with GANs based on Radial Basis Functions
Motivated by the problems of existing approaches and inspired by the success of the generative adversarial networks (GANs) in the field of computer vision, we prove an optimal discriminator theorem that the optimal discriminator of a GAN is a radial basis function neural network (RBFNN) while dealing with nonlinear sparse FFD regression and generation.
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization.
