Neural architecture search (NAS) has become increasingly popular in the deep learning
community recently, mainly because it can provide an opportunity to allow interested users …

Deep learning has been successful in automating the design of features in machine learning
pipelines. However, the algorithms optimizing neural network parameters remain largely …

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum
advantages for practical problems under the quantum–classical hybrid computational …

Regression is a powerful tool to accurately predict the outcome metric of a system given a
set of parameters, but has traditionally been restricted to methods which are only applicable …

Neural architecture search (NAS) is a promising technique to design efficient and high-
performance deep neural networks (DNNs). As the performance requirements of ML …

The success of neural networks (NNs) is tightly linked with their architectural design—a
complex problem by itself. We here introduce a novel framework leveraging Graph Neural …

Neural architecture search (NAS) has recently been addressed from various directions,
including discrete, sampling-based methods and efficient differentiable approaches. While …

This paper discusses the challenges of the hyperparameter tuning in deep learning models
and proposes a green approach to the neural architecture search process that minimizes its …

As the security issue of cloud-offloaded Deep Learning (DL) inference is drawing increasing
attention, running DL inference in Trusted Execution Environments (TEEs) has become a …

Predicting the accuracy of candidate neural architectures is an important capability of NAS-
based solutions. When a candidate architecture has properties that are similar to other …