Conventional image classification methods usually require a large number of training
samples for the training model. However, in practical scenarios, the amount of available …

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 …

Abstract Invariant Risk Minimization (IRM) is an emerging invariant feature extracting
technique to help generalization with distributional shift. However, we find that there exists a …

Neural Architecture Search (NAS) has received increasing attention because of its
exceptional merits in automating the design of Deep Neural Network (DNN) architectures …

It is of great significance to estimate the performance of a given model architecture without
training in the application of Neural Architecture Search (NAS) as it may take a lot of time to …

The success of multi-task learning (MTL) can largely be attributed to the shared
representation of related tasks, allowing the models to better generalise. In deep learning …

Evolutionary neural architecture search (ENAS) methods have been successfully used to
design convolutional neural network (CNN) architectures automatically. These methods …

Abstract Information networks generally exhibit three characteristics, namely dynamicity,
heterogeneity, and node attribute diversity. However, most existing network embedding …

Networks found with neural architecture search (NAS) achieve the state-of-the-art
performance in a variety of tasks, out-performing human-designed networks. However, most …

Neural architecture search (NAS) can automatically discover well-performing architectures
in a large search space and has been shown to bring improvements to various applications …