The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Federated learning is a recently proposed distributed machine learning paradigm for privacy
preservation, which has found a wide range of applications where data privacy is of primary …

We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its
advantages over existing NAS approaches. Existing one-shot method, however, is hard to …

Data augmentation (DA) has been widely utilized to improve generalization in training deep
neural networks. Recently, human-designed data augmentation has been gradually …

With the emergence of deep neural networks, many research fields, such as image
classification, object detection, speech recognition, natural language processing, machine …

Many recently proposed methods for Neural Architecture Search (NAS) can be formulated
as bilevel optimization. For efficient implementation, its solution requires approximations of …

Imbalanced datasets are commonplace in modern machine learning problems. The
presence of under-represented classes or groups with sensitive attributes results in …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Convolutional neural networks (CNNs) are a superb computing paradigm in deep learning,
and their architectures are considered to be the key to performance breakthroughs in …

The performance of deep neural networks is heavily dependent on its architecture and
various neural architecture search strategies have been developed for automated network …