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individual search methods have been replaced by weight-sharing search methods for higher …

Neural architecture search (NAS) has a great impact by automatically designing effective
neural network architectures. However, the prohibitive computational demand of …

Neural architecture search (NAS) is inherently subject to the gap of architectures during
searching and validating. To bridge this gap, we develop Differentiable ArchiTecture …

Latest algorithms for automatic neural architecture search perform remarkable but are
basically directionless in search space and computational expensive in the training of every …

Automatic Machine Learning (AutoML) is a powerful mechanism to design and tune models.
We present Katib, a scalable Kubernetes-native general AutoML platform that can support a …

Neural architecture search (NAS) is inherently subject to the gap of architectures during
searching and validating. To bridge this gap effectively, we develop Differentiable …

While usage of convolutional neural networks (CNN) is widely prevalent, methods proposed
so far always have considered homogeneous kernels for this task. In this paper, we propose …

For network architecture search (NAS), it is crucial but challenging to simultaneously
guarantee both effectiveness and efficiency. Towards achieving this goal, we develop a …

Large-scale artificial neural networks have many redundant structures, making the network
fall into the issue of local optimization and extended training time. Moreover, existing neural …

AMLA is an Automatic Machine Learning frAmework for implementing and deploying neural
architecture search algorithms. Neural architecture search algorithms are AutoML algorithms …