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Found 70 papers, 35 papers with code
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V
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.
Federated Deep Multi-View Clustering with Global Self-Supervision
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
In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
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.
Ranked #1 on
Text Summarization
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Deep Multi-View Subspace Clustering with Anchor Graph
To significantly reduce the complexity, we construct an anchor graph with small size for each view.
Hierarchical State Abstraction Based on Structural Information Principles
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations.
A Comparison of Image Denoising Methods
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.
Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition
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.
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
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.
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
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.
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
Our code and data can be found at https://github. com/ERNetERNet/ERNet
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants.
Variational Graph Generator for Multi-View Graph Clustering
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
Deep Clustering: A Comprehensive Survey
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task.
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
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
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.
Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis
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
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.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs.
Task Modifiers for HTN Planning and Acting
The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments.
Deep learning for drug repurposing: methods, databases, and applications
Drug development is time-consuming and expensive.
Structure-Preserving Graph Kernel for Brain Network Classification
This paper presents a novel graph-based kernel learning approach for connectome analysis.
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model.
Multiplex Graph Networks for Multimodal Brain Network Analysis
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
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
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.
Multi-level Feature Learning for Contrastive Multi-view Clustering
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.
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
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
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.
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Due to inevitable sensor failures, data in each view may contain error.
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph.
Federated Multi-View Learning for Private Medical Data Integration and Analysis
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).
Non-Linear Fusion for Self-Paced Multi-View Clustering
In NSMVC, we directly assign different exponents to different views according to their qualities.
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
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.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
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.
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
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.
FedMood: Federated Learning on Mobile Health Data for Mood Detection
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.
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Graph representation learning has attracted increasing research attention.
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
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
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.
A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods
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.
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation
Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing.
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
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.
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
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.
Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
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.
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
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
To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector.
A Survey on Text Classification: From Shallow to Deep Learning
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups.
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification.
Adversarial Attack and Defense on Graph Data: A Survey
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.
Boosted Sparse and Low-Rank Tensor Regression
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
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
Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model.
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
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.
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Network analysis of human brain connectivity is critically important for understanding brain function and disease states.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.
Error-Robust Multi-View Clustering
Various types of errors behave differently and inconsistently in each view.
Contaminant Removal for Android Malware Detection Systems
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
Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis.
Kernelized Support Tensor Machines
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
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition, neuroimaging tensor data has been the focus of intense investigation.
Learning from Multi-View Multi-Way Data via Structural Factorization Machines
Real-world relations among entities can often be observed and determined by different perspectives/views.
Online Multi-view Clustering with Incomplete Views
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
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?
Multi-Source Multi-View Clustering via Discrepancy Penalty
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
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.
