Comprehensive map reveals how E3 ligases organize protein cleanup in human cells

Individual mammalian cells contain billions of protein molecules, which must be synthesized, deployed, and removed with ...
IEEE

GURNet: A Gated U-Shaped Encoder-Decoder Predictor for Reversible Data Hiding

Abstract: The existing deep learning based reversible data hiding (RDH) predictors typically adopt standard convolutions for extracting features, which inherently fails to capture contextual ...
Beebom

What is Deep Learning in AI? Understanding How AI Really Works

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
GitHub

Deep Reinforcement Learning for Service Function Chain Placement with Graph Attention and Transformer Encoder

This repository contains the implementation of the paper of paper Deep Reinforcement Learning for Service Function Chain Placement with Graph Attention and Transformer Encoder. In this paper, we ...
Scientific Research Publishing

Chen, H., Zhang, Y., Kalra, M.K., Lin, F., Chen, Y., Liao, P., et al. (2017) Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network. IEEE Transactions on ...

ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...
Scientific Research Publishing

Feng, L., Zhao, C. and Sun, Y. (2021) Dual Attention-Based Encoder-Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development. IEEE Transactions on Neural ...

ABSTRACT: In this paper, a novel multilingual OCR (Optical Character Recognition) method for scanned papers is provided. Current open-source solutions, like Tesseract, offer extremely high accuracy ...
GitHub

Sparse Transfer Learning in Encoder Decoder Transfomer

I am currently doing a small research for my study on Sparse Transfer Learning and SparseML library is a good approach for my work. My topic is about applying sparse transfer learning on different ...
Frontiers

A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study

Objective: To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. Methods: We retrospectively ...