Search Results for author:
Found 5 papers, 3 papers with code
Studying the Practices of Testing Machine Learning Software in the Wild
Aims: To fill this gap, we perform the first fine-grained empirical study on ML testing practices in the wild, to identify the ML properties being tested, the followed testing strategies, and their implementation throughout the ML workflow.
Detection and Evaluation of bias-inducing Features in Machine learning
In this study, we propose an approach for systematically identifying all bias-inducing features of a model to help support the decision-making of domain experts.
An Empirical Study on the Usage of Automated Machine Learning Tools
Machine learning (ML) practitioners use AutoML tools to automate and optimize the process of feature engineering, model training, and hyperparameter optimization and so on.
An Empirical Study of Challenges in Converting Deep Learning Models
Usually DL models are developed and trained using DL frameworks that have their own internal mechanisms/formats to represent and train DL models, and usually those formats cannot be recognized by other frameworks.
Studying the Practices of Deploying Machine Learning Projects on Docker
Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is used to deploy ML-based projects.
