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Found 13 papers, 1 papers with code
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results.
Deep Feature Learning for Wireless Spectrum Data
In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures.
Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
We also show a 12 percentage point performance advantage of the proposed DL based model over a random forest model and observe performance degradation with the increase of the number of devices in the household, namely with each additional 5 devices, the average performance degrades by approximately 7 percentage points.
XAI for Self-supervised Clustering of Wireless Spectrum Activity
In this paper, we propose a methodology for explaining deep clustering, self-supervised learning architectures comprised of a representation learning part based on a Convolutional Neural Network (CNN) and a clustering part.
On-Premise Artificial Intelligence as a Service for Small and Medium Size Setups
Artificial Intelligence (AI) technologies are moving from customized deployments in specific domains towards generic solutions horizontally permeating vertical domains and industries.
Resource-aware Deep Learning for Wireless Fingerprinting Localization
Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes.
Self-supervised Learning for Clustering of Wireless Spectrum Activity
In particular, we compare the performance of two SSL models, one based on a reference DeepCluster architecture and one adapted for spectrum activity identification and clustering, and a baseline model based on K-means clustering algorithm.
On Designing Data Models for Energy Feature Stores
We first propose a taxonomy for designing data models suitable for energy applications, explain how this model can support the design of features and their subsequent management by specialized feature stores.
Towards Sustainable Deep Learning for Wireless Fingerprinting Localization
A detailed performance evaluation shows that the proposed model producesonly 58 % of the carbon footprint while maintaining 98. 7 % of the overall performance compared to state of the art model external to our group.
Learning to Fairly Classify the Quality of Wireless Links
Machine learning (ML) has been used to develop increasingly accurate link quality estimators for wireless networks.
Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network
Multiple-input multiple-output (MIMO) is an enabling technology to meet the growing demand for faster and more reliable communications in wireless networks with a large number of terminals, but it can also be applied for position estimation of a terminal exploiting multipath propagation from multiple antennas.
Learning to Detect Anomalous Wireless Links in IoT Networks
After decades of research, the Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health.
Machine Learning for Wireless Link Quality Estimation: A Survey
The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling link quality estimation are becoming increasingly sophisticated.
