Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to
effectively handle joint optimization of modules in automated machine learning (AutoML) …

Predicting neural architecture performance is a challenging task and is crucial to neural
architecture design and search. Existing approaches either rely on neural performance …

Evaluating neural network performance is critical to deep neural network design but a costly
procedure. Neural predictors provide an efficient solution by treating architectures as …

Nowadays, meta-heuristic and machine learning algorithms are often used for a variety of
tasks in cloud computing operations. The choice of hyper-parameter values has a direct …

The relationship between architecture and performance is critical for improving the efficiency
of neural architecture design, yet few efforts have been devoted to understanding this …

The Neural Architecture Search (NAS) enables Deep Learning engineers to automatically
explore the wide variety of all possible neural architectures to design the solution for a given …