Search Results for author: Yaochu Jin

Found 30 papers, 2 papers with code

Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times

no code implementations30 Aug 2021 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer

Most existing multiobjetive evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time.

Transfer Learning

PIVODL: Privacy-preserving vertical federated learning over distributed labels

no code implementations25 Aug 2021 Hangyu Zhu, Rui Wang, Yaochu Jin, Kaitai Liang

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.

Federated Learning

A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization

no code implementations22 Jun 2021 Jinjin Xu, Yaochu Jin, Wenli Du

Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization.

Federated Learning

Federated Learning on Non-IID Data: A Survey

no code implementations12 Jun 2021 Hangyu Zhu, Jinjin Xu, Shiqing Liu, Yaochu Jin

Federated learning is an emerging distributed machine learning framework for privacy preservation.

Federated Learning

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

Information Maximization Clustering via Multi-View Self-Labelling

1 code implementation12 Mar 2021 Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas

Specifically, the proposed clustering objective employs mutual information, and maximizes the dependency between the integrated discrete representation and a discrete probability distribution.

Image Classification Image Clustering +1

Tiny Adversarial Mulit-Objective Oneshot Neural Architecture Search

no code implementations28 Feb 2021 Guoyang Xie, Jinbao Wang, Guo Yu, Feng Zheng, Yaochu Jin

Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources.

Fine-tuning Neural Architecture Search

A Federated Data-Driven Evolutionary Algorithm

no code implementations16 Feb 2021 Jinjin Xu, Yaochu Jin, Wenli Du, Sai Gu

Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems.

Federated Learning

Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

no code implementations16 Jan 2021 Jia Liu, Yaochu Jin

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans.

Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification

no code implementations10 Jan 2021 Yan Xiao, Yaochu Jin, Kuangrong Hao

First, based on the prototypical networks, we propose an adaptive mixture mechanism to add label words to the representation of the class prototype, which, to the best of our knowledge, is the first attempt to integrate the label information into features of the support samples of each class so as to get more interactive class prototypes.

Few-Shot Relation Classification Representation Learning

Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments

no code implementations14 Dec 2020 Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou Chai

Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments.

Ensemble Learning Outlier Detection

Image Clustering using an Augmented Generative Adversarial Network and Information Maximization

no code implementations8 Nov 2020 Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas

Image clustering has recently attracted significant attention due to the increased availability of unlabelled datasets.

Deep Clustering Image Clustering

From Federated Learning to Federated Neural Architecture Search: A Survey

no code implementations12 Sep 2020 Hangyu Zhu, Haoyu Zhang, Yaochu Jin

Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern.

Distributed, Parallel, and Cluster Computing

Generation of Consistent Sets of Multi-Label Classification Rules with a Multi-Objective Evolutionary Algorithm

no code implementations27 Mar 2020 Thiago Zafalon Miranda, Diorge Brognara Sardinha, Márcio Porto Basgalupp, Yaochu Jin, Ricardo Cerri

Recently, the interest in interpretable classification models has grown, partially as a consequence of regulations such as the General Data Protection Regulation.

Classification General Classification +1

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

no code implementations10 Mar 2020 Yan Xiao, Yaochu Jin, Ran Cheng, Kuangrong Hao

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

Relation Extraction

Ternary Compression for Communication-Efficient Federated Learning

1 code implementation7 Mar 2020 Jinjin Xu, Wenli Du, Ran Cheng, Wangli He, Yaochu Jin

To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor.

Federated Learning Quantization

Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search

no code implementations7 Mar 2020 Haoyu Zhang, Yaochu Jin, Ran Cheng, Kuangrong Hao

Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms.

Global Optimization Neural Architecture Search

Real-time Federated Evolutionary Neural Architecture Search

no code implementations4 Mar 2020 Hangyu Zhu, Yaochu Jin

Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning.

Federated Learning Neural Architecture Search

A Survey of Deep Learning Applications to Autonomous Vehicle Control

no code implementations23 Dec 2019 Sampo Kuutti, Richard Bowden, Yaochu Jin, Phil Barber, Saber Fallah

However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios.

Autonomous Vehicles Object Detection +1

Identification of Interaction Clusters Using a Semi-supervised Hierarchical Clustering Method

no code implementations20 Oct 2019 Yu Chen, Yuanyuan Yang, Yaochu Jin, Xiufen Zou

Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc.

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.

Multi-objective Evolutionary Federated Learning

no code implementations18 Dec 2018 Hangyu Zhu, Yaochu Jin

A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks.

Federated Learning

A Particle Filter based Multi-Objective Optimization Algorithm: PFOPS

no code implementations28 Aug 2018 Bin Liu, Yaochu Jin

To this end, we make an effort to extend the scope of application of the PFO paradigm to multi-objective optimization (MOO) cases.

Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results

no code implementations8 Jun 2017 Yuan Yuan, Yew-Soon Ong, Liang Feng, A. K. Qin, Abhishek Gupta, Bingshui Da, Qingfu Zhang, Kay Chen Tan, Yaochu Jin, Hisao Ishibuchi

In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously.

Multiobjective Optimization

An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network

no code implementations20 Feb 2017 Joseph Chrol-Cannon, Yaochu Jin, André Grüning

This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation.

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

no code implementations4 Jan 2017 Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin

To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators.

Multiobjective Optimization

Cannot find the paper you are looking for? You can Submit a new open access paper.