Search Results for author:
Found 7 papers, 3 papers with code
Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows
Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data.
SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification
Despite the success of deep neural networks (DNNs) in enabling on-device voice assistants, increasing evidence of bias and discrimination in machine learning is raising the urgency of investigating the fairness of these systems.
Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence
Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence.
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
While internal clustering validation measures are well established in the electricity domain, they are limited for selecting useful clusters.
Design Considerations for High Impact, Automated Echocardiogram Analysis
Deep learning has the potential to automate echocardiogram analysis for early detection of heart disease.
Using competency questions to select optimal clustering structures for residential energy consumption patterns
During cluster analysis domain experts and visual analysis are frequently relied on to identify the optimal clustering structure.
Machine Learning Systems for Intelligent Services in the IoT: A Survey
Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services.
