Search Results for author: Fang Chen

Found 47 papers, 7 papers with code

ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks

no code implementations29 Sep 2023 Yiqiao Li, Jianlong Zhou, Yifei Dong, Niusha Shafiabady, Fang Chen

Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery.

Decision Making

Ethical ChatGPT: Concerns, Challenges, and Commandments

no code implementations18 May 2023 Jianlong Zhou, Heimo Müller, Andreas Holzinger, Fang Chen

Large language models, e. g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population.


Meta-learning approaches for few-shot learning: A survey of recent advances

no code implementations13 Mar 2023 Hassan Gharoun, Fereshteh Momenifar, Fang Chen, Amir H. Gandomi

Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction.

Few-Shot Learning

Genetic Imitation Learning by Reward Extrapolation

no code implementations3 Jan 2023 Boyuan Zheng, Jianlong Zhou, Fang Chen

Imitation learning demonstrates remarkable performance in various domains.

Imitation Learning

Explaining Imitation Learning through Frames

no code implementations3 Jan 2023 Boyuan Zheng, Jianlong Zhou, Chunjie Liu, Yiqiao Li, Fang Chen

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains.

Explainable artificial intelligence Imitation Learning

GANExplainer: GAN-based Graph Neural Networks Explainer

no code implementations30 Dec 2022 Yiqiao Li, Jianlong Zhou, Boyuan Zheng, Fang Chen

With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making.

Decision Making Graph Classification +2

DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

no code implementations19 Dec 2022 Fang Chen, Heiko Balzter, Feixiang Zhou, Peng Ren, Huiyu Zhou

In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images.

Image Segmentation Semantic Segmentation

Self-Attentive Pooling for Efficient Deep Learning

no code implementations16 Sep 2022 Fang Chen, Gourav Datta, Souvik Kundu, Peter Beerel

With the aggressive down-sampling of the activation maps in the initial layers (providing up to 22x reduction in memory consumption), our approach achieves 1. 43% higher test accuracy compared to SOTA techniques with iso-memory footprints.

Instance Image Retrieval by Learning Purely From Within the Dataset

no code implementations12 Aug 2022 Zhongyan Zhang, Lei Wang, Yang Wang, Luping Zhou, Jianjia Zhang, Peng Wang, Fang Chen

Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained.

Image Retrieval Retrieval +1

Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour

no code implementations7 Aug 2022 Feixiang Zhou, Xinyu Yang, Fang Chen, Long Chen, Zheheng Jiang, Hui Zhu, Reiko Heckel, Haikuan Wang, Minrui Fei, Huiyu Zhou

Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism.

Representation Learning Self-Supervised Learning

De-biased Representation Learning for Fairness with Unreliable Labels

no code implementations1 Aug 2022 Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen

In other words, the fair pre-processing methods ignore the discrimination encoded in the labels either during the learning procedure or the evaluation stage.

Fairness Representation Learning

A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics

no code implementations26 Jul 2022 Yiqiao Li, Jianlong Zhou, Sunny Verma, Fang Chen

Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data.

Detection of magnetohydrodynamic waves by using machine learning

no code implementations15 Jun 2022 Fang Chen, Ravi Samtaney

Identification of different types of MHD waves is an important and challenging task in such complex wave patterns.

BIG-bench Machine Learning regression +1

Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds

no code implementations29 Sep 2021 Fangcen Liu, Chenqiang Gao, Fang Chen, Deyu Meng, WangMeng Zuo, Xinbo Gao

We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range.

Evolutionary dynamics of zero-determinant strategies in repeated multiplayer games

no code implementations14 Sep 2021 Fang Chen, Te Wu, Long Wang

Here, we use a newly proposed state-clustering method to theoretically analyze the evolutionary dynamics of two representative ZD strategies: generous ZD strategies and extortionate ZD strategies.

State-clustering method of payoff computation in repeated multiplayer games

no code implementations24 Aug 2021 Fang Chen, Te Wu, Guocheng Wang, Long Wang

In this paper, we propose a new method, namely, the state-clustering method to calculate the long-term payoffs in repeated games.


Local Patch Network with Global Attention for Infrared Small Target Detection

1 code implementation13 Aug 2021 Fang Chen, Chenqiang Gao, Fangcen Liu, Yue Zhao, Yuxi Zhou, Deyu Meng, WangMeng Zuo

A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images.

Semantic Segmentation

Bias-Tolerant Fair Classification

no code implementations7 Jul 2021 Yixuan Zhang, Feng Zhou, Zhidong Li, Yang Wang, Fang Chen

Therefore, we propose a Bias-TolerantFAirRegularizedLoss (B-FARL), which tries to regain the benefits using data affected by label bias and selection bias.

Classification Fairness +2

Imitation Learning: Progress, Taxonomies and Challenges

no code implementations23 Jun 2021 Boyuan Zheng, Sunny Verma, Jianlong Zhou, Ivor Tsang, Fang Chen

Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors.

Autonomous Driving Imitation Learning

Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation

no code implementations15 Apr 2021 Jianlong Zhou, Weidong Huang, Fang Chen

The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections.

BIG-bench Machine Learning Feature Importance

Variational Co-embedding Learning for Attributed Network Clustering

no code implementations15 Apr 2021 Shuiqiao Yang, Sunny Verma, Borui Cai, Jiaojiao Jiang, Kun Yu, Fang Chen, Shui Yu

Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space.

Clustering Node Clustering +1

Boosted Genetic Algorithm using Machine Learning for traffic control optimization

no code implementations11 Mar 2021 Tuo Mao, Adriana-Simona Mihaita, Fang Chen, Hai L. Vu

Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework.

BIG-bench Machine Learning

Novel structural evolution of several nanolaminate Mn+1AXn (n=1, 2, 3, etc.) ceramics under pressure from first principles

no code implementations22 Dec 2020 Ben-Yang Li, Fang Chen, Heng-Na Xiong, Ling Tang, Ju-Xiang Shao, Ze-Jin Yang

We did extensive research for the typical nanolaminate Mn+1AXn (n=1, 2, 3) ceramics focusing on the structural stability, the phase transition pressure of Ti2GaN (160 GPa) is far higher than that of Zr2GaN (92 GPa), meaning the strong M dependence of the same group, whereas Zr2AlN (98 GPa) has similar value with that of Zr2GaN, meaning the weak A dependence.

Materials Science

Structured Context Enhancement Network for Mouse Pose Estimation

1 code implementation1 Dec 2020 Feixiang Zhou, Zheheng Jiang, Zhihua Liu, Fang Chen, Long Chen, Lei Tong, Zhile Yang, Haikuan Wang, Minrui Fei, Ling Li, Huiyu Zhou

However, quantifying mouse behaviours from videos or images remains a challenging problem, where pose estimation plays an important role in describing mouse behaviours.

Animal Pose Estimation

Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis

1 code implementation16 Oct 2020 Sunny Verma, Jiwei Wang, Zhefeng Ge, Rujia Shen, Fan Jin, Yang Wang, Fang Chen, Wei Liu

In this research, we first propose a common network to discover both intra-modal and inter-modal dynamics by utilizing basic LSTMs and tensor based convolution networks.

Multimodal Sentiment Analysis Sentiment Classification

Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia

no code implementations22 Jun 2020 Jianlong Zhou, Shuiqiao Yang, Chun Xiao, Fang Chen

In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period.

Sentiment Analysis

Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers

no code implementations5 Jun 2020 Dilusha Weeraddana, Bin Liang, Zhidong Li, Yang Wang, Fang Chen, Livia Bonazzi, Dean Phillips, Nitin Saxena

Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year.

BIG-bench Machine Learning

An improved online learning algorithm for general fuzzy min-max neural network

no code implementations8 Jan 2020 Thanh Tung Khuat, Fang Chen, Bogdan Gabrys

This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries.

General Classification

Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation

no code implementations29 Oct 2019 Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen

In this paper, we consider the sigmoid Gaussian Hawkes process model: the baseline intensity and triggering kernel of Hawkes process are both modeled as the sigmoid transformation of random trajectories drawn from Gaussian processes (GP).

Bayesian Inference Gaussian Processes +1

Machine Learning in/for Blockchain: Future and Challenges

no code implementations12 Sep 2019 Fang Chen, Hong Wan, Hua Cai, Guang Cheng

Machine learning and blockchain are two of the most noticeable technologies in recent years.

BIG-bench Machine Learning

An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network

1 code implementation29 May 2019 Thanh Tung Khuat, Fang Chen, Bogdan Gabrys

Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction.

Efficient EM-Variational Inference for Hawkes Process

no code implementations29 May 2019 Feng Zhou, Zhidong Li, Xuhui Fan, Yang Wang, Arcot Sowmya, Fang Chen

In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility.

Variational Inference

Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data

no code implementations14 May 2019 Weida Li, Mingxia Liu, Fang Chen, Daoqiang Zhang

Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains.

A Unified Neural Network Model for Geolocating Twitter Users

no code implementations CONLL 2018 Mohammad Ebrahimi, Elaheh ShafieiBavani, Raymond Wong, Fang Chen

Locations of social media users are important to many applications such as rapid disaster response, targeted advertisement, and news recommendation.

Disaster Response News Recommendation

AMC: Attention guided Multi-modal Correlation Learning for Image Search

2 code implementations CVPR 2017 Kan Chen, Trung Bui, Fang Chen, Zhaowen Wang, Ram Nevatia

According to the intent of query, attention mechanism can be introduced to adaptively balance the importance of different modalities.

Image Retrieval

Infinite Hidden Semi-Markov Modulated Interaction Point Process

no code implementations NeurIPS 2016 Matt Zhang, Peng Lin, Ting Guo, Yang Wang, Fang Chen

The proposed approach can simultaneously model both the observations and arrival times of temporal events, and determine the number of latent states from data.

Stochastic Patching Process

no code implementations23 May 2016 Xuhui Fan, Bin Li, Yi Wang, Yang Wang, Fang Chen

Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions.

On Improving Informativity and Grammaticality for Multi-Sentence Compression

no code implementations7 May 2016 Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen

In this paper, we propose an effective approach to enhance the word graph-based MSC and tackle the issue that most of the state-of-the-art MSC approaches are confronted with: i. e., improving both informativity and grammaticality at the same time.

Language Modelling POS +2

Stable Learning in Coding Space for Multi-Class Decoding and Its Extension for Multi-Class Hypothesis Transfer Learning

no code implementations CVPR 2014 Bang Zhang, Yi Wang, Yang Wang, Fang Chen

Many prevalent multi-class classification approaches can be unified and generalized by the output coding framework which usually consists of three phases: (1) coding, (2) learning binary classifiers, and (3) decoding.

General Classification Multi-class Classification +1

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