This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Explainability is a hot topic nowadays for artificial intelligent (AI) systems. The role of
machine learning (ML) models on influencing human decisions shed light on the back-box …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Ablation studies provide insights into the relative contribution of different architectural and
regularization components to machine learning models' performance. In this paper, we …

As the need for machine learning (ML) increases rapidly across all industry sectors, there is
a significant interest among commercial database providers to support" Query 2.0", which …

Abstract Machine learning enables computers to learn from data and fuels artificial
intelligence systems with capabilities to make even super-human decisions. Yet, despite …

According to a mainstream position in contemporary cognitive science and philosophy, the
use of abstract compositional concepts is amongst the most characteristic indicators of …

It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous
definition and fair metrics. In this paper, we briefly summarize the status quo of the metrics …

Machine learning tasks over image databases often generate masks that annotate image
content (eg, saliency maps, segmentation maps, depth maps) and enable a variety of …

Deep learning enables numerous applications across diverse areas. Data systems
researchers are also increasingly experimenting with deep learning to enhance data …