Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …

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individual search methods have been replaced by weight-sharing search methods for higher …

Differentiable architecture search (DARTS) provided a fast solution in finding effective
network architectures, but suffered from large memory and computing overheads in jointly …

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

The growing interest in both the automation of machine learning and deep learning has
inevitably led to the development of a wide variety of automated methods for neural …

Quantum architecture search (QAS) is the process of automating architecture engineering of
quantum circuits. It has been desired to construct a powerful and general QAS platform …

Abstract Differentiable ARchiTecture Search (DARTS) has recently become the mainstream
in the neural architecture search (NAS) due to its efficiency and simplicity. With a gradient …

This paper introduces a novel optimization method for differential neural architecture search,
based on the theory of prediction with expert advice. Its optimization criterion is well fitted for …

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

Abstract One-Shot Neural Architecture Search (NAS) significantly improves the
computational efficiency through weight sharing. However, this approach also introduces …