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Found 26 papers, 1 papers with code
Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360
Fairness AI aims to detect and alleviate bias across the entire AI development life cycle, encompassing data curation, modeling, evaluation, and deployment-a pivotal aspect of ethical AI implementation.
Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification
In the N2C2 2022 competitions, various tasks were presented to promote the identification of key factors in electronic health records (EHRs) using the Contextualized Medication Event Dataset (CMED).
Inverse Quantum Fourier Transform Inspired Algorithm for Unsupervised Image Segmentation
Image segmentation is a very popular and important task in computer vision.
Underwater Acoustic Communication Channel Modeling using Reservoir Computing
Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment.
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.
Effect of Batch Normalization on Noise Resistant Property of Deep Learning Models
The fast execution speed and energy efficiency of analog hardware has made them a strong contender for deployment of deep learning model at the edge.
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models
However, significant performance degradation suffered by deep learning models due to the inherent noise present in the analog computation can limit their use in mission-critical applications.
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models
In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model building block, in designing DNN models with excellent noise-resistant property is proposed.
Underwater Acoustic Communication Channel Modeling using Deep Learning
With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important.
Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection
Moreover, it cannot fully involve user feedback in the loop of learning detection models for further enhancing fake news detection.
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks
In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic.
Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces
Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts.
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.
A Survey of Complex-Valued Neural Networks
Artificial neural networks (ANNs) based machine learning models and especially deep learning models have been widely applied in computer vision, signal processing, wireless communications, and many other domains, where complex numbers occur either naturally or by design.
Adversarial Machine Learning in Wireless Communications using RF Data: A Review
In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented.
Benchmarking Inference Performance of Deep Learning Models on Analog Devices
Analog hardware implemented deep learning models are promising for computation and energy constrained systems such as edge computing devices.
Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs
This motivates us to consider offloading this type of deep learning (DL) tasks to a mobile edge computing (MEC) server due to limited computational resource and energy budget of the UAV, and further improve the inference accuracy.
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.
Efficient Privacy Preserving Edge Computing Framework for Image Classification
In order to extract knowledge from the large data collected by edge devices, traditional cloud based approach that requires data upload may not be feasible due to communication bandwidth limitation as well as privacy and security concerns of end users.
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.
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
By combining the proposed data pre-processing method with the ensemble model, better performance of rumor detection has been demonstrated in the experiments using PHEME dataset.
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.
Two-path Deep Semi-supervised Learning for Timely Fake News Detection
In addition, we build a shared CNN to extract the low level features on both labeled data and unlabeled data to feed them into these two paths.
Deep Two-path Semi-supervised Learning for Fake News Detection
News in social media such as Twitter has been generated in high volume and speed.
Hierarchical Transfer Convolutional Neural Networks for Image Classification
In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification.
