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Found 7 papers, 0 papers with code
Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation
Image segmentation is a very popular and important task in computer vision.
Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image
However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated.
Semi-supervised Learning for COVID-19 Image Classification via ResNet
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community.
Computation Offloading in Multi-Access Edge Computing Networks: A Multi-Task Learning Approach
Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES).
Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework
This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint.
Cell Type Identification from Single-Cell Transcriptomic Data via Semi-supervised Learning
Then genes in these sentences are represented as gene embeddings to reduce data sparsity.
Device Authentication Codes based on RF Fingerprinting using Deep Learning
Specifically, an autoencoder is used to automatically extract features from the RF traces, and the reconstruction error is used as the DAC and this DAC is unique to the device and the particular message of interest.
