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Two-stage structural information enhancement for source-free domain adaptation
Source-free domain adaptation (SFDA) uses models trained from source domains to solve similar tasks in unlabeled domains, without accessing source...
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Word Representation Learning
Words are the building blocks of phrases, sentences, and documents. Word representation is thus critical for natural language processing (NLP). In...
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Ni-DehazeNet: representation learning via bilevel optimized architecture search for nighttime dehazing
Nighttime dehazing is a challenging ill-posed problem due to the severe haze pollution and color attenuation. Since available daytime dehazing...
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Overview of indoor scene recognition and representation methods based on multimodal knowledge graphs
This paper provides a comprehensive overview of multi-modal knowledge graph technology and a three-layer framework for scene recognition. Integrating...
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On the effectiveness of log representation for log-based anomaly detection
Logs are an essential source of information for people to understand the running status of a software system. Due to the evolving modern software...
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Graph-Enriched Biomedical Entity Representation Transformer
Infusing external domain-specific knowledge about diverse biomedical concepts and relationships into language models (LMs) advances their ability to...
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Collaborative representation induced broad learning model for classification
The broad learning system (BLS) is a novel flat neural network that is fast and effective in various pattern recognition and classification...
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Structure-adaptive graph neural network with temporal representation and residual connections
Graph learning methods have boosted brain analysis for user healthcare, disease detection, and behavioral modeling. Spatially separated brain regions...
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Low-light image enhancement with geometrical sparse representation
Low-light image enhancement (LLIE) can improve the visibility of low-light images. Low-light images exhibit a series of visual degradation, such as...
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Multi-scale Structural Asymmetric Convolution for Wireframe Parsing
Extracting salient line segments with their corresponding junctions is a promising method for structural environment recognition. However,...
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LegalHTML: A Representation Language for Legal Acts
The Publications Office (OP) of European Union (EU) expressed the need to simplify the Official Journal production workflow, which required different...
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Causal Reasoning Meets Visual Representation Learning: A Prospective Study
Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal...
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Global-local neighborhood based network representation for citation recommendation
Many researchers study citation recommendation approaches using network representation recently. It learns low-dimensional vector representation of...
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Overview of Structural Decision Diagrams
This chapter presents a short history of the development of decision diagrams and compares the traditional BDDs, here referred to as functional BDDs,...
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Structural learning of simple staged trees
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are...
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Simultaneous instance pooling and bag representation selection approach for multiple-instance learning (MIL) using vision transformer
In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no...
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Local spatial continuity steered sparse representation for occluded face recognition
Recently, sparse representation in face recognition has been widely studied in computer vision. For face identification under complex conditions,...
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Structural Decision Diagrams in Digital Test Theory and Applications
This is the first book that sums up test-related modeling of digital circuits and systems by a new structural-decision-diagrams model. The model...
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ASBiNE: Dynamic Bipartite Network Embedding for incorporating structural and attribute information
Graph representation learning (GRL) has recently gained attention and becoming popular in research community. GRL has been proven to be extremely...
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A Representation Learning Link Prediction Approach Using Line Graph Neural Networks
Link prediction problem aims to infer the potential future links between two nodes in the network. Most of the existing methods exhibit limited...