Junliang Du
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View article: Information-Theoretic Greedy Layer-wise Training for Traffic Sign Recognition
Information-Theoretic Greedy Layer-wise Training for Traffic Sign Recognition Open
Modern deep neural networks (DNNs) are typically trained with a global cross-entropy loss in a supervised end-to-end manner: neurons need to store their outgoing weights; training alternates between a forward pass (computation) and a top-d…
View article: Federated Graph Neural Networks for Heterogeneous Graphs with Data Privacy and Structural Consistency
Federated Graph Neural Networks for Heterogeneous Graphs with Data Privacy and Structural Consistency Open
This paper addresses the problem of joint modeling for multi-source heterogeneous graph data in distributed environments by proposing a federated graph neural network classification framework driven by structural alignment and consistency …
View article: Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment
Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment Open
This paper addresses the challenges of high computational cost and slow inference in deploying large language models. It proposes a distillation strategy guided by multiple teacher models. The method constructs several teacher models and i…
View article: Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining
Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining Open
Complex data mining has wide application value in many fields, especially in the feature extraction and classification tasks of unlabeled data. This paper proposes an algorithm based on self-supervised learning and verifies its effectivene…
View article: Multi-Scale Transformer Architecture for Accurate Medical Image Classification
Multi-Scale Transformer Architecture for Accurate Medical Image Classification Open
This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medical image analysis. By integrating a multi-scale feature fus…
View article: Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion
Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion Open
This paper proposes a cold start recommendation model that integrates contrastive learning, aiming to solve the problem of performance degradation of recommendation systems in cold start scenarios due to the scarcity of user and item inter…
View article: A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models
A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models Open
This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditional machine learning models in environments with uneven class…
View article: An effective vessel segmentation method using SLOA-HGC
An effective vessel segmentation method using SLOA-HGC Open
View article: Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data Open
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-sc…
View article: Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches Open
This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges posed by small-sample data. Recognizing the critical limitat…
View article: Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models Open
Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high variability across patients. Deep learning has improved se…
View article: Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs
Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs Open
This study proposed a knowledge graph entity extraction and relationship reasoning algorithm based on a graph neural network, using a graph convolutional network and graph attention network to model the complex structure in the knowledge g…
View article: Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis
Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis Open
This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention mechanism to process Visual Field (VF) data, enabling the ex…
View article: Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm Open
This paper explores the application of knowledge distillation technology in target detection tasks, especially the impact of different distillation temperatures on the performance of student models. By using YOLOv5l as the teacher network …
View article: Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation
Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation Open
This paper introduces a novel deep-learning method for the automatic detection and segmentation of lung nodules, aimed at advancing the accuracy of early-stage lung cancer diagnosis. The proposed approach leverages a unique "Channel Squeez…
View article: Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification
Dual-Branch Dynamic Graph Convolutional Network for Robust Multi-Label Image Classification Open
For the intricate task of multi-label image classification, this paper introduces an innovative approach: an attention-guided dual-branch dynamic graph convolutional network. This methodology is designed to address the difficulties faced b…
View article: What is the Ordinal Priority Approach?
What is the Ordinal Priority Approach? Open
Humans, in their most important role as decision-makers, make choices every day. Simple choices don't even require a calculator, but more complicated ones require some calculation. Complicated choices, such as the ones where one has to cho…
View article: Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China
Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China Open
The coordinated promotion of urban digitalization and green development is an inevitable requirement for sustainable development in the digital age. Based on the coupling mechanism of urban digitalization and green development, in this stu…
View article: Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners
Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners Open
We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the …
View article: Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners
Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners Open
We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the …
View article: A novel grey object matrix incidence clustering model for panel data and its application
A novel grey object matrix incidence clustering model for panel data and its application Open
In order to fully excavate the information contained in the multi-index panel data, one take decision objects as the research object, and the development state matrix and the development speed matrix of the decision objects are defined by …
View article: Assessing Regional Differences in Green Innovation Efficiency of Industrial Enterprises in China
Assessing Regional Differences in Green Innovation Efficiency of Industrial Enterprises in China Open
Green technology innovation is an important means to break out of the constraints of resources and the environment, enhance the competitiveness of enterprises, and achieve the upgrading of industrial structures, and promote high-quality ec…
View article: Interaction Detection with Bayesian Decision Tree Ensembles
Interaction Detection with Bayesian Decision Tree Ensembles Open
Methods based on Bayesian decision tree ensembles have proven valuable in constructing high-quality predictions, and are particularly attractive in certain settings because they encourage low-order interaction effects. Despite adapting to …