356 Result(s)
within
English
Artificial Intelligence
Data Mining and Knowledge Discovery
**-**a Ding
Chapter
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Chapter and Conference Paper
Knowledge Graph Completion via Subgraph Topology Augmentation
Knowledge graph completion (KGC) has achieved widespread success as a key technique to ensure high-quality structured knowledge for downstream tasks (e.g., recommendation systems and question answering). Howev...
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Chapter and Conference Paper
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous efforts have been made to minimize domain divergence, most e...
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Chapter and Conference Paper
LNFGP: Local Node Fusion-Based Graph Partition by Greedy Clustering
Graph partitioning manages large RDF datasets in various applications such as file systems, databases and distributed computing frameworks. Research on graph partitioning can be generally categorized into two ...
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Chapter
Sentence and Document Representation Learning
Sentence and document are high-level linguistic units of natural languages. Representation learning of sentences and documents remains a core and challenging task because many important applications of natural...
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Chapter
Ten Key Problems of Pre-trained Models: An Outlook of Representation Learning
The aforementioned representation learning methods have shown their effectiveness in various NLP scenarios and tasks. Large-scale pre-trained language models (i.e., big models) are the state of the art of repr...
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Chapter
Pre-trained Models for Representation Learning
Pre-training-fine-tuning has recently become a new paradigm in natural language processing, learning better representations of words, sentences, and documents in a self-supervised manner. Pre-trained models no...
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Chapter
Representation Learning for Compositional Semantics
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Chapter and Conference Paper
Improving Knowledge Graph Embedding Using Dynamic Aggregation of Neighbor Information
Knowledge graph embedding represents the embedding of entities and relations in the knowledge graph into a low-dimensional vector space to accomplish the knowledge graph complementation task. Most existing kno...
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Chapter and Conference Paper
A Machine-Learning Framework for Supporting Content Recommendation via User Feedback Data and Content Profiles in Content Managements Systems
Matrix Factorization (MF) which is a Collaborative Filtering (CF) based model, is widely used in Recommendation Systems (RS). In this research, we deal with a specific recommendation problem of recommending conte...
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Chapter and Conference Paper
ID-Agnostic User Behavior Pre-training for Sequential Recommendation
Recently, sequential recommendation has emerged as a widely studied topic. Existing researches mainly design effective neural architectures to model user behavior sequences based on item IDs. However, this kin...
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Chapter and Conference Paper
GERNS: A Graph Embedding with Repeat-Free Neighborhood Structure for Subgraph Matching Optimization
Subgraph matching is used to determine whether a query graph exists within a target graph, and appears in a lot applications of domains including social sciences, chemistry, biology and database systems. Exist...
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Chapter and Conference Paper
A Machine-Learning Approach to Recognizing Teaching Beliefs in Narrative Stories of Outstanding Professors
The coding of text information to recognize the teaching beliefs of outstanding professors is crucial research to enhance teaching performance in university. Most previous studies adopted manual coding, and th...
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Chapter and Conference Paper
A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises
In the collaborative production environment of manufacturing tasks, the evaluation of enterprise technical capability in advance has a direct impact on the high-performance collaboration between the supplier a...
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Chapter and Conference Paper
A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph
Timeliness is one of the important indicators of data quality. In industrial production processes, a large amount of dependent data is generated, often resulting in unclear timestamps. Therefore, this article ...
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Chapter and Conference Paper
Design of an Automated CNN Composition Scheme with Lightweight Convolution for Space-Limited Applications
The emergence of the CNN network has enabled many networks for image object recognition, object segmentation, etc., and has brought amazing results to image processing tasks, including MaskRCNN [4] and YOLO [8]. ...
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Chapter and Conference Paper
Data Representation and Clustering with Double Low-Rank Constraints
High-dimensional data are usually drawn from an union of multiple low-dimensional subspaces. Low-rank representation (LRR), as a multi-subspace structure learning method, uses low rank constraints to extract t...
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Chapter and Conference Paper
Graph Convolutional Neural Network Based on Channel Graph Fusion for EEG Emotion Recognition
To represent the unstructured relationships among EEG channels, graph neural networks are proposed to classify EEG signal. Currently most graph neural networks learn the relationships between EEG channels usin...
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Chapter and Conference Paper
Graph Contrastive Learning with Hybrid Noise Augmentation for Recommendation
Recommendation System is one of the effective tools to solve the problem of information overload in the era of big data, but the data sparsity has greatly affected its performance. Recently, contrastive learni...
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Chapter and Conference Paper
Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement
Multimodal Named Entity Recognition (MNER) aims to use images to locate and classify named entities in a given free text. The mainstream MNER method based on a pre-trained model ignores the syntactic relations...
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Chapter and Conference Paper
Breast Cancer Histopathology Image Classification Using Frequency Attention Convolution Network
The existing deep learning works mainly capture breast cancer histopathology image features in the spatial domain, and they rarely consider the frequency domain feature representation of histopathology images....
