… We design a new automatic architecture design algorithm named as neural architecture
optimization (NAO), which performs the optimization within continuous space (using gradient …

… types of optimization methods: reinforcement learning, evolutionary algorithms, surrogate
model-based optimization, and one-shot architecture search. We define these optimization …

… with millions of weights for supervised learning tasks, SGD-based weight optimization … for
optimizing weights and solely use evolutionary algorithms for optimizing the neural architecture …

… function optimization strategies. In each iteration of BO, our goal is to find the neural architecture
… Evaluating the acquisition function for every neural architecture in the search space is …

… architecture space into a continuously differentiable form, and further uses gradient
optimization techniques to search the neural architecture. This approach mainly focuses on the …

… architecture optimization method, called Neural Architecture Transformer (NAT), to optimize
any given architecture … Note that the architecture optimization on an entire network is still very …

… [28] went through a review of neuroevolution, aiming at revealing the weight optimization
rather than the architecture of neural networks. Besides, most of the references in these …

… a whole and by benchmarking a range of architecture optimization algorithms. … neural
architecture search that is inexpensive to evaluate but still preserves the original NAS optimization …

… In this paper, we propose a novel framework enabling Bayesian optimization to guide the
network morphism for efficient neural architecture search. The framework develops a neural …

… neural architecture search. NSGA-Net affords a number of practical benefits: (1) the design
of neural network architectures that can effectively optimize … than optimizing weighted linear …