Search Results for author: Yaochu Jin

Found 53 papers, 12 papers with code

Evolutionary Reinforcement Learning: A Survey

no code implementations7 Mar 2023 Hui Bai, Ran Cheng, Yaochu Jin

This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL).

Board Games Hyperparameter Optimization +3

Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition

no code implementations28 Feb 2023 Xueming Yan, Zhihang Fang, Yaochu Jin

While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to multilingual scene text recognition due to the complexities in the visual appearance of multilingual texts.

Language Identification Scene Text Recognition

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

1 code implementation31 Jan 2023 Guoyang Xie, Jinbao Wang, Jiaqi Liu, Jiayi Lyu, Yong liu, Chengjie Wang, Feng Zheng, Yaochu Jin

We realize that the lack of actual IM settings most probably hinders the development and usage of these methods in real-world applications.

Anomaly Detection Continual Learning +1

EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

1 code implementation29 Jan 2023 Beichen Huang, Ran Cheng, Yaochu Jin, Kay Chen Tan

Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices.

OpenAI Gym

Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

no code implementations28 Jan 2023 Guoyang Xie, Jingbao Wang, Jiaqi Liu, Feng Zheng, Yaochu Jin

Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection.

Anomaly Detection

Deep Industrial Image Anomaly Detection: A Survey

1 code implementation27 Jan 2023 Jiaqi Liu, Guoyang Xie, Jingbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, Yaochu Jin

In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets.

Anomaly Detection

A Graph Neural Network with Negative Message Passing for Graph Coloring

no code implementations26 Jan 2023 Xiangyu Wang, Xueming Yan, Yaochu Jin

In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems.

Intelligent Computing: The Latest Advances, Challenges and Future

no code implementations21 Nov 2022 Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan

In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.

End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

no code implementations27 Oct 2022 Shiqing Liu, Xueming Yan, Yaochu Jin

The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively.

Combinatorial Optimization

A Secure Federated Data-Driven Evolutionary Multi-objective Optimization Algorithm

no code implementations15 Oct 2022 Qiqi Liu, Yuping Yan, Peter Ligeti, Yaochu Jin

To address this issue, this paper proposes a secure federated data-driven evolutionary multi-objective optimization algorithm to protect both the raw data and the newly infilled solutions obtained by optimizing the acquisition function conducted on the server.

Alleviating Search Bias in Bayesian Evolutionary Optimization with Many Heterogeneous Objectives

no code implementations25 Aug 2022 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer

To this end, we develop a multi-objective Bayesian evolutionary optimization approach to HE-MOPs by exploiting the different data sets on the cheap and expensive objectives in HE-MOPs to alleviate the search bias caused by the heterogeneous evaluation costs for evaluating different objectives.

Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues

no code implementations23 Aug 2022 Nan Li, Lianbo Ma, Guo Yu, Bing Xue, Mengjie Zhang, Yaochu Jin

Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem.

AutoML Feature Engineering

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

no code implementations8 Aug 2022 Zhichao Lu, Ran Cheng, Yaochu Jin, Kay Chen Tan, Kalyanmoy Deb

From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.

Multiobjective Optimization Neural Architecture Search

Towards Fairness-Aware Multi-Objective Optimization

no code implementations22 Jul 2022 Guo Yu, Lianbo Ma, Wei Du, Wenli Du, Yaochu Jin

Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications.

BIG-bench Machine Learning Decision Making +2

Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures

no code implementations12 Jul 2022 Jia Liu, Ran Cheng, Yaochu Jin

First, we formulate the NAS problem for enhancing adversarial robustness of deep neural networks into a multiobjective optimization problem.

Adversarial Robustness Multiobjective Optimization +1

Recent Advances in Bayesian Optimization

no code implementations7 Jun 2022 Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer

Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.


A Survey on Computationally Efficient Neural Architecture Search

no code implementations3 Jun 2022 Shiqing Liu, Haoyu Zhang, Yaochu Jin

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs).

Neural Architecture Search

Cross-Modality Neuroimage Synthesis: A Survey

no code implementations14 Feb 2022 Guoyang Xie, Jinbao Wang, Yawen Huang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, Yaochu Jin

In this paper, we are the first one to comprehensively approach cross-modality neuroimage synthesis task from different perspectives, which include the level of the supervision (especially for weakly-supervised and unsupervised), loss function, evaluation metrics, the range of modality synthesis, datasets (aligned, private and public) and the synthesis-based downstream tasks.

Image Generation

A Survey of Visual Sensory Anomaly Detection

1 code implementation14 Feb 2022 Xi Jiang, Guoyang Xie, Jinbao Wang, Yong liu, Chengjie Wang, Feng Zheng, Yaochu Jin

In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.

Anomaly Detection

A Generic Self-Supervised Framework of Learning Invariant Discriminative Features

no code implementations14 Feb 2022 Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas

Specifically, the prior transformation functions are replaced with a self-transformation mechanism, derived through an unsupervised training process of adversarial training, for imposing invariant representations.

Contrastive Learning Dimensionality Reduction +2

FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss

1 code implementation29 Jan 2022 Jinbao Wang, Guoyang Xie, Yawen Huang, Yefeng Zheng, Yaochu Jin, Feng Zheng

The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.

Data Augmentation Image Generation +1

FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

1 code implementation22 Jan 2022 Guoyang Xie, Jinbao Wang, Yawen Huang, Yuexiang Li, Yefeng Zheng, Feng Zheng, Yaochu Jin

There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.

Federated Learning Image Generation +1

FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels

no code implementations29 Sep 2021 Rui Wang, Oğuzhan Ersoy, Hangyu Zhu, Yaochu Jin, Kaitai Liang

Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.

Federated Learning

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 Privacy Preserving

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

1 code implementation22 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.

BIG-bench Machine Learning 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.


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.

Neural Architecture Search

A Federated Data-Driven Evolutionary Algorithm

1 code implementation16 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 Management

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

1 code implementation8 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

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.

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.

BIG-bench Machine Learning Federated Learning +1

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 +2

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

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