Tag: machine learning
Machine Learning (ML) deals with how computer programs can learn and improve at performing a specific task when trained on historical data. ML is divided into unsupervised learning (such as clustering and topic modeling) and supervised learning (training a system on annotated examples). ML has been a central part of my research since my PhD studies, and even ended up in my thesis title.
Can RE Help to Better Prepare Industrial AI for the Commercial Scale?
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN
Requirements Engineering for Machine Learning: Perspectives from Data Scientists
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
SZZ Unleashed: An Open Implementation of the SZZ Algorithm – Featuring Example Usage in a Study of Just-in-Time Bug Prediction for the Jenkins Project
Machine Learning to Guide Performance Testing: An Autonomous Test Framework
Towards Structured Evaluation of Deep Neural Network Supervisors
Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
