most citedMulti-objective Neural Architecture Search with Almost No Training

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

cs.CV20201 cited

Multi-objective Neural Architecture Search with Almost No Training

Shengran Hu, Ran Cheng, Cheng He +1

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the steady stream of impressive empirical results…

cs.CL2020

Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction

Yan Xiao, Yaochu Jin, Ran Cheng +1

With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information. As…

cs.NE2020

Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search

Haoyu Zhang, Yaochu Jin, Ran Cheng +1

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

cs.NE2019

Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Cheng He, Shihua Huang, Ran Cheng +2

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is hig…

cs.NE2019

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

Cheng He, Shihua Huang, Ran Cheng +2

Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is h…