Search Results for author: Lifang He

Found 70 papers, 35 papers with code

Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V

1 code implementation29 Oct 2023 Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, Lichao Sun

In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i. e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task.

Language Modelling Large Language Model +2

Federated Deep Multi-View Clustering with Global Self-Supervision

no code implementations24 Sep 2023 Xinyue Chen, Jie Xu, Yazhou Ren, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, Lifang He

Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.


Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

1 code implementation5 Sep 2023 Guangjie Zeng, Hao Peng, Angsheng Li, Zhiwei Liu, Chunyang Liu, Philip S. Yu, Lifang He

In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.

Lesion Segmentation Outlier Detection +2

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

1 code implementation26 May 2023 Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Adhikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin Zhou, Xiang Li, Lifang He, Brian D. Davison, Quanzheng Li, Yong Chen, Hongfang Liu, Lichao Sun

In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.

Image Captioning Medical Visual Question Answering +2

Deep Multi-View Subspace Clustering with Anchor Graph

1 code implementation11 May 2023 Chenhang Cui, Yazhou Ren, Jingyu Pu, Xiaorong Pu, Lifang He

To significantly reduce the complexity, we construct an anchor graph with small size for each view.

Clustering Contrastive Learning +1

Hierarchical State Abstraction Based on Structural Information Principles

1 code implementation24 Apr 2023 Xianghua Zeng, Hao Peng, Angsheng Li, Chunyang Liu, Lifang He, Philip S. Yu

State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations.

Continuous Control Decision Making +1

A Comparison of Image Denoising Methods

1 code implementation18 Apr 2023 Zhaoming Kong, Fangxi Deng, Haomin Zhuang, Jun Yu, Lifang He, Xiaowei Yang

In this paper, to investigate the applicability of existing denoising techniques, we compare a variety of denoising methods on both synthetic and real-world datasets for different applications.

Benchmarking Image Denoising

Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition

no code implementations21 Mar 2023 Zhenqian Wu, Xiaoyuan Li, Yazhou Ren, Xiaorong Pu, Xiaofeng Zhu, Lifang He

In order to better learn these neutral expression-disentangled features (NDFs) and to alleviate the non-convex optimization problem, a self-paced learning (SPL) strategy based on NDFs is proposed in the training stage.

Facial Expression Recognition

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration

1 code implementation6 Dec 2022 Yao Su, Xin Dai, Lifang He, Xiangnan Kong

Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image.

Deformable Medical Image Registration Image Registration +1

Variational Graph Generator for Multi-View Graph Clustering

no code implementations13 Oct 2022 Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He

The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.

Clustering Graph Clustering

Deep Clustering: A Comprehensive Survey

no code implementations9 Oct 2022 Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He

Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.

Clustering Deep Clustering

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

1 code implementation23 Sep 2022 Jun Yu, Zhaoming Kong, Liang Zhan, Li Shen, Lifang He

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task.

feature selection regression

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

1 code implementation30 Jun 2022 Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang

Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.

Disease Prediction

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

1 code implementation9 Jun 2022 Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang

Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.


Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs

no code implementations8 May 2022 Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He

To address this issue, in this paper we propose Deep Embedded Multi-view Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ), which utilizes the latent graphs to promote the performance of deep embedded MVC models from two aspects.

Clustering Representation Learning

Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis

no code implementations27 Apr 2022 Houliang Zhou, Lifang He, Yu Zhang, Li Shen, Brian Chen

Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies.

Deep reinforcement learning guided graph neural networks for brain network analysis

no code implementations18 Mar 2022 Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica J. M. Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.

reinforcement-learning Reinforcement Learning (RL) +1

Task Modifiers for HTN Planning and Acting

1 code implementation9 Feb 2022 Weihang Yuan, Hector Munoz-Avila, Venkatsampath Raja Gogineni, Sravya Kondrakunta, Michael Cox, Lifang He

The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments.

Multiplex Graph Networks for Multimodal Brain Network Analysis

1 code implementation31 Jul 2021 Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He

In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

1 code implementation11 Jul 2021 Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang

Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.

Disease Prediction

Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

no code implementations7 Jul 2021 Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.

Contrastive Learning

Multi-level Feature Learning for Contrastive Multi-view Clustering

1 code implementation CVPR 2022 Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, Lifang He

Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.

Clustering Contrastive Learning

Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering

no code implementations ICCV 2021 Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He

The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.


A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

Federated Multi-View Learning for Private Medical Data Integration and Analysis

no code implementations4 May 2021 Sicong Che, Hao Peng, Lichao Sun, Yong Chen, Lifang He

This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV).

Data Integration Federated Learning +2

Non-Linear Fusion for Self-Paced Multi-View Clustering

no code implementations19 Apr 2021 Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lifang He

In NSMVC, we directly assign different exponents to different views according to their qualities.


Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

1 code implementation16 Apr 2021 JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

Clustering Node Classification +2

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks

1 code implementation14 Apr 2021 Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr

FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.

Federated Learning Molecular Property Prediction

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

1 code implementation2 Apr 2021 Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He

Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.

Clustering Event Detection

FedMood: Federated Learning on Mobile Health Data for Mood Detection

1 code implementation6 Feb 2021 Xiaohang Xu, Hao Peng, Lichao Sun, Md Zakirul Alam Bhuiyan, Lianzhong Liu, Lifang He

Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.

BIG-bench Machine Learning Depression Detection +3

Heterogeneous Similarity Graph Neural Network on Electronic Health Records

no code implementations17 Jan 2021 Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, Philip S. Yu

EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.

CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning

no code implementations10 Dec 2020 Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang, Liang Zhan

In this paper, we propose a new interpretable graph pooling framework - CommPOOL, that can capture and preserve the hierarchical community structure of graphs in the graph representation learning process.

Graph Classification Graph Representation Learning

A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods

no code implementations6 Nov 2020 Zhaoming Kong, Xiaowei Yang, Lifang He

Leveraging the nonlocal self-similarity (NLSS) characteristic of images and sparse representation in the transform domain, the block-matching and 3D filtering (BM3D) based methods show powerful denoising performance.

Benchmarking Image Denoising

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation

1 code implementation COLING 2020 Zhongfen Deng, Hao Peng, Congying Xia, JianXin Li, Lifang He, Philip S. Yu

Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.

Decision Making

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

no code implementations COLING 2020 Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He

Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

Data Augmentation Image Classification

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

1 code implementation26 Sep 2020 Ye Liu, Yao Wan, Lifang He, Hao Peng, Philip S. Yu

To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output.

Graph Attention Text Generation

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market

1 code implementation12 Aug 2020 Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He

As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation.

Adversarial Directed Graph Embedding

1 code implementation9 Aug 2020 Shijie Zhu, JianXin Li, Hao Peng, Senzhang Wang, Lifang He

To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.

Graph Embedding Graph Mining +1

Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College

1 code implementation10 Jun 2020 Chen Li, Xutan Peng, Hao Peng, Jian-Xin Li, Lihong Wang, Philip S. Yu, Lifang He

Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.

Graph Attention Graph Generation

Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Lifang He, Bo Li, Lihong Wang, Philip S. Yu

In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.

General Classification Multi Label Text Classification +3

Adversarial Attack and Defense on Graph Data: A Survey

1 code implementation26 Dec 2018 Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Yixin Liu, Philip S. Yu, Lifang He, Bo Li

Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.

Adversarial Attack Image Classification +1

Boosted Sparse and Low-Rank Tensor Regression

2 code implementations NeurIPS 2018 Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, Fei Wang

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.


A Self-Organizing Tensor Architecture for Multi-View Clustering

no code implementations18 Oct 2018 Lifang He, Chun-Ta Lu, Yong Chen, Jiawei Zhang, Linlin Shen, Philip S. Yu, Fei Wang

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other.


Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

no code implementations11 Sep 2018 Jian-Guo Zhang, Ji Wang, Lifang He, Zhao Li, Philip S. Yu

Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.

Anomaly Detection Cloud Computing

Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks

no code implementations11 Sep 2018 Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu

Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.

Network Embedding Tensor Decomposition

Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis

no code implementations19 Jun 2018 Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow

Network analysis of human brain connectivity is critically important for understanding brain function and disease states.

Clustering Graph Embedding

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

1 code implementation22 May 2018 Xi Sheryl Zhang, Lifang He, Kun Chen, Yuan Luo, Jiayu Zhou, Fei Wang

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.

Error-Robust Multi-View Clustering

no code implementations1 Jan 2018 Mehrnaz Najafi, Lifang He, Philip S. Yu

Various types of errors behave differently and inconsistently in each view.


Contaminant Removal for Android Malware Detection Systems

no code implementations7 Nov 2017 Lichao Sun, Xiaokai Wei, Jiawei Zhang, Lifang He, Philip S. Yu, Witawas Srisa-an

The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy

Cryptography and Security

Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

no code implementations12 Sep 2017 Guixiang Ma, Chun-Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin

Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis.

Clustering Graph Embedding +3

Kernelized Support Tensor Machines

no code implementations ICML 2017 Lifang He, Chun-Ta Lu, Guixiang Ma, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks.

Multi-Way Multi-Level Kernel Modeling for Neuroimaging Classification

no code implementations CVPR 2017 Lifang He, Chun-Ta Lu, Hao Ding, Shen Wang, Linlin Shen, Philip S. Yu, Ann B. Ragin

Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.

Classification General Classification

Learning from Multi-View Multi-Way Data via Structural Factorization Machines

no code implementations10 Apr 2017 Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, Philip S. Yu

Real-world relations among entities can often be observed and determined by different perspectives/views.

Online Multi-view Clustering with Incomplete Views

no code implementations2 Nov 2016 Weixiang Shao, Lifang He, Chun-Ta Lu, Philip S. Yu

We model the multi-view clustering problem as a joint weighted nonnegative matrix factorization problem and process the multi-view data chunk by chunk to reduce the memory requirement.


Online Unsupervised Multi-view Feature Selection

no code implementations27 Sep 2016 Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu

Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams?

Clustering feature selection +1

Multi-Source Multi-View Clustering via Discrepancy Penalty

no code implementations14 Apr 2016 Weixiang Shao, Jiawei Zhang, Lifang He, Philip S. Yu

In many real-world applications, information can be gathered from multiple sources, while each source can contain multiple views, which are more cohesive for learning.


DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages

no code implementations31 Jul 2014 Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang

The dual-tensorial mapping function can map each tensor instance in the input space to another tensor in the feature space while preserving the tensorial structure.

General Classification

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