Search Results for author: Cheng He

Found 10 papers, 2 papers with code

Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation

no code implementations8 Oct 2021 Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu, Jing Wang, Miao Zhang

For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for both academia and industries. Due to the costly stochastic gradient descent (SGD) training of CNNs for performance evaluation, most existing NAS methods are computationally expensive for real-world deployments.

Neural Architecture Search

Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

no code implementations22 May 2021 Ye Tian, Xingyi Zhang, Cheng He, Kay Chen Tan, Yaochu Jin

In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems.

Translation

Multi-objective Neural Architecture Search with Almost No Training

no code implementations27 Nov 2020 Shengran Hu, Ran Cheng, Cheng He, Zhichao Lu

In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries.

Neural Architecture Search Transfer Learning

RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning

2 code implementations14 Sep 2020 Hao Tan, Ran Cheng, Shihua Huang, Cheng He, Changxiao Qiu, Fan Yang, Ping Luo

Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.

Keypoint Detection Neural Architecture Search +2

Multimodal Image-to-Image Translation via a Single Generative Adversarial Network

no code implementations4 Aug 2020 Shihua Huang, Cheng He, Ran Cheng

Existing multimodal I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only.

Image-to-Image Translation Translation

Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments

no code implementations20 Dec 2019 Shujie Han, Jun Wu, Erci Xu, Cheng He, Patrick P. C. Lee, Yi Qiang, Qixing Zheng, Tao Huang, Zixi Huang, Rui Li

To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e. g., Backblaze SMART logs).

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)

no code implementations11 Oct 2019 Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin

The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

no code implementations10 Jul 2019 Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, Yaochu Jin

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 highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset

no code implementations5 Oct 2018 Gengchen Mai, Krzysztof Janowicz, Cheng He, Sumang Liu, Ni Lao

To test a system's ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question.

Information Retrieval Question Answering +1

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