12 citations · 23 across the 12 of their papers we have counts for
6 papers · 1 filter
Understanding Feedback Mechanisms in Machine Learning Jupyter Notebooks
Arumoy Shome, Luis Cruz, Diomidis Spinellis +1
The machine learning development lifecycle is characterized by iterative and exploratory processes that rely on feedback mechanisms to ensure data and model integrity. Despite the…
Green Runner: A tool for efficient deep learning component selection
Jai Kannan
For software that relies on machine-learned functionality, model selection is key to finding the right model for the task with desired performance characteristics. Evaluating a mod…
McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions
Lorena Poenaru-Olaru, Luis Cruz, Jan Rellermeyer +1
Due to the continuous change in operational data, AIOps solutions suffer from performance degradation over time. Although periodic retraining is the state-of-the-art technique to p…
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
Arumoy Shome, Luis Cruz, Arie van Deursen
Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the tr…
Towards Automatic Translation of Machine Learning Visual Insights to Analytical Assertions
Arumoy Shome, Luis Cruz, Arie van Deursen
We present our vision for developing an automated tool capable of translating visual properties observed in Machine Learning (ML) visualisations into Python assertions. The tool ai…
Energy Patterns for Web: An Exploratory Study
Pooja Rani, Jonas Zellweger, Veronika Kousadianos +3
As the energy footprint generated by software is increasing at an alarming rate, understanding how to develop energy-efficient applications has become a necessity. Previous work ha…