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Combine Patterns
In the last 23 chapters I have explained the individual patterns. Now we will deal with an example for which we want to use several patterns at the...
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Representation learning: serial-autoencoder for personalized recommendation
Nowadays, the personalized recommendation has become a research hotspot for addressing information overload. Despite this, generating effective...
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Graph neural news recommendation based on multi-view representation learning
Accurate news representation is of crucial importance in personalized news recommendation. Most of existing news recommendation model lack...
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Data representation learning via dictionary learning and self-representation
Dictionary learning is an effective feature learning method, leading to many remarkable results in data representation and classification tasks....
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Federated unsupervised representation learning
To leverage the enormous amount of unlabeled data on distributed edge devices, we formulate a new problem in federated learning called federated...
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Meaning Representation
Before the study of semantic analysis, this chapter explores meaning representation, a vital component in NLP before the discussion of semantic and...
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Online content-based sequential recommendation considering multimodal contrastive representation and dynamic preferences
The online content, including live streaming and short videos, provides abundant visual and textual product information to users, which offers...
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Recent advances in implicit representation-based 3D shape generation
Various techniques have been developed and introduced to address the pressing need to create three-dimensional (3D) content for advanced applications...
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A representation learning model based on stochastic perturbation and homophily constraint
The network representation learning task of fusing node multi-dimensional classification information aims to effectively combine node...
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Multi-scale hash encoding based neural geometry representation
Recently, neural implicit function-based representation has attracted more and more attention, and has been widely used to represent surfaces using...
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Node and edge dual-masked self-supervised graph representation
Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in...
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Deep video representation learning: a survey
This paper provides a review on representation learning for videos . We classify recent spatio-temporal feature learning methods for sequential visual...
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Large-scale knowledge graph representation learning
The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks....
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Multimodal fuzzy granular representation and classification
AbstractIn a complex classification task, samples are represented by various types of multimodal features, including structured data, text, images,...
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Classifier subset selection based on classifier representation and clustering ensemble
Ensemble pruning can improve the performance and reduce the storage requirements of an integration system. Most ensemble pruning approaches remove...
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Advancements in number representation for high-precision computing
Efficient representation of data is a fundamental prerequisite for addressing computational problems effectively using computers. The continual...
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Template-centric deep linear discriminant analysis for visual representation
In some real-world visual recognition tasks, instances are generated according to certain standards, which should serve as references during instance...
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IMPRL-Net: interpretable multi-view proximity representation learning network
Due to the heterogeneity gap in multi-view data, researchers have been attempting to apply these data to learn a co-latent representation to bridge...
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Property graph representation learning for node classification
Graph representation learning (graph embedding) has led to breakthrough results in various machine learning graph-based applications such as node...
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Metric learning and local enhancement based collaborative representation for hyperspectral image classification
Collaborative Representation (CR) models have been successfully employed for Hyperspectral Images (HSIs) classification because of the effectiveness...
