5 papers
Automating Neural Architecture Design without Search
Zixuan Liang, Yanan Sun
Neural structure search (NAS), as the mainstream approach to automate deep neural architecture design, has achieved much success in recent years. However, the performance estimatio…
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
Xiangning Xie, Yuqiao Liu, Yanan Sun +3
Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few ye…
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Qing Ye, Yuxuan Han, Yanan sun +1
Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs)…
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
Yuqiao Liu, Yanan Sun, Bing Xue +1
Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classifica…
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures
Yanan Sun, Ziyao Ren, Gary G. Yen +3
The superiority of Convolutional Neural Networks (CNNs) largely relies on their architectures that are often manually crafted with extensive human expertise. Unfortunately, such ki…