Search Results for author: Ling Wang

Found 26 papers, 5 papers with code

3D Implicit Transporter for Temporally Consistent Keypoint Discovery

1 code implementation10 Sep 2023 Chengliang Zhong, Yuhang Zheng, Yupeng Zheng, Hao Zhao, Li Yi, Xiaodong Mu, Ling Wang, Pengfei Li, Guyue Zhou, Chao Yang, Xinliang Zhang, Jian Zhao

To address this issue, the Transporter method was introduced for 2D data, which reconstructs the target frame from the source frame to incorporate both spatial and temporal information.

Learning to Branch in Combinatorial Optimization with Graph Pointer Networks

no code implementations4 Jul 2023 Rui Wang, Zhiming Zhou, Tao Zhang, Ling Wang, Xin Xu, Xiangke Liao, Kaiwen Li

The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions.

Combinatorial Optimization Variable Selection

Enhancing the Robustness of QMIX against State-adversarial Attacks

no code implementations3 Jul 2023 Weiran Guo, Guanjun Liu, Ziyuan Zhou, Ling Wang, Jiacun Wang

To increase the robustness of multi-agent reinforcement learning (MARL) algorithms, we train models using a variety of attacks in this research.

Multi-agent Reinforcement Learning reinforcement-learning

MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks

1 code implementation17 May 2023 Wenfang Sun, Yingjun Du, XianTong Zhen, Fan Wang, Ling Wang, Cees G. M. Snoek

To account for the uncertainty caused by the limited training tasks, we propose a variational MetaModulation where the modulation parameters are treated as latent variables.

Few-Shot Learning

Coevolutionary Framework for Generalized Multimodal Multi-objective Optimization

1 code implementation2 Dec 2022 Wenhua Li, Xingyi Yao, Kaiwen Li, Rui Wang, Tao Zhang, Ling Wang

To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs.

Evolutionary Algorithms Transfer Learning

HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention

1 code implementation30 Aug 2022 Weimin Zhu, Yi Zhang, Duancheng Zhao, Jianrong Xu, Ling Wang

Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge.

Drug Discovery Molecular Property Prediction +2

Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm

no code implementations18 Jul 2022 Wenhua Li, Shengjun Huang, Tao Zhang, Rui Wang, Ling Wang

Binary matrix optimization commonly arise in the real world, e. g., multi-microgrid network structure design problem (MGNSDP), which is to minimize the total length of the power supply line under certain constraints.

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

no code implementations8 May 2022 Hanxuan Cai, Huimin Zhang, Duancheng Zhao, Jingxing Wu, Ling Wang

In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model.

Molecular Property Prediction Property Prediction

Temporal Convolution Domain Adaptation Learning for Crops Growth Prediction

no code implementations24 Feb 2022 Shengzhe Wang, Ling Wang, ZhiHao Lin, Xi Zheng

We are the first to use the temporal convolution filters as the backbone to construct a domain adaptation network architecture which is suitable for deep learning regression models with very limited training data of the target domain.

Domain Adaptation

Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?

no code implementations25 Dec 2021 Isaac Ronald Ward, Ling Wang, Juan lu, Mohammed Bennamoun, Girish Dwivedi, Frank M Sanfilippo

Using XAI, we quantified the contribution that specific drugs had on these ACS predictions, thus creating an XAI-based technique for pharmacovigilance monitoring, using ACS as an example of the adverse outcome to detect.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

Optimal Expansion of Business Opportunity

no code implementations13 Dec 2021 Ling Wang, Kexin Chen, Mei Choi Chiu, Hoi Ying Wong

The length of the waiting period is related to the opportunity cost, return, and risk of the expanded business.

Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling

no code implementations26 Jul 2021 Liang Zeng, Lei Wang, Hui Niu, Ruchen Zhang, Ling Wang, Jian Li

In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform.

Metric Learning Time Series Analysis

LAI Estimation of Cucumber Crop Based on Improved Fully Convolutional Network

no code implementations16 Apr 2021 Weiqi Shu, Ling Wang, Bolong Liu, Jie Liu

How to measure LAI accurately and efficiently is the key to the crop yield estimation problem.

A Deep Learning-Based Approach to Extracting Periosteal and Endosteal Contours of Proximal Femur in Quantitative CT Images

no code implementations3 Feb 2021 Yu Deng, Ling Wang, Chen Zhao, Shaojie Tang, Xiaoguang Cheng, Hong-Wen Deng, Weihua Zhou

In this study, we proposed an approach based on deep learning for the automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments.

Interactive Segmentation

Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things

no code implementations15 Oct 2020 Ling Wang, Cheng Zhang, Zejian Luo, ChenGuang Liu, Jie Liu, Xi Zheng, Athanasios Vasilakos

To reduce the computational cost without loss of generality, we present a defense strategy called a progressive defense against adversarial attacks (PDAAA) for efficiently and effectively filtering out the adversarial pixel mutations, which could mislead the neural network towards erroneous outputs, without a-priori knowledge about the attack type.

Volterra mortality model: Actuarial valuation and risk management with long-range dependence

no code implementations21 Sep 2020 Ling Wang, Mei Choi Chiu, Hoi Ying Wong

While abundant empirical studies support the long-range dependence (LRD) of mortality rates, the corresponding impact on mortality securities are largely unknown due to the lack of appropriate tractable models for valuation and risk management purposes.


Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization

no code implementations2 Aug 2020 Yu-Jun Zheng, Xin Chen, Tie-Er Gan, Min-Xia Zhang, Wei-Guo Sheng, Ling Wang

In this paper, we present an approach that first transforms the original high-dimensional, constrained multiobjective optimization problem to a low-dimensional, unconstrained multiobjective optimization problem, and then evaluates each solution to the transformed problem by solving a set of simple single-objective optimization subproblems, such that the problem can be efficiently solved by existing evolutionary multiobjective algorithms.

Evolutionary Algorithms Multiobjective Optimization

Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the "Wrong" Predictions

no code implementations2 Aug 2019 Hongliang Duan, Ling Wang, Chengyun Zhang, Jianjun Li

We cast retrosynthesis as a machine translation problem by introducing a special Tensor2Tensor, an entire attention-based and fully data-driven model.

Machine Translation NMT +2

Sample-Efficient Policy Learning based on Completely Behavior Cloning

no code implementations9 Nov 2018 Qiming Zou, Ling Wang, Ke Lu, Yu Li

Direct policy search is one of the most important algorithm of reinforcement learning.

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