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Debiased graph contrastive learning based on positive and unlabeled learning
Graph contrastive learning (GCL) is one of the mainstream techniques for unsupervised graph representation learning, which reduces the distance...
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Joint data augmentations for automated graph contrastive learning and forecasting
Graph augmentation plays a crucial role in graph contrastive learning. However, existing methods primarily optimize augmentations specific to...
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Knowledge graph-enhanced molecular contrastive learning with functional prompt
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient....
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SimDCL: dropout-based simple graph contrastive learning for recommendation
Representation learning of users and items is the core of recommendation, and benefited from the development of graph neural network (GNN), graph...
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Node classification in complex networks based on multi-view debiased contrastive learning
In complex networks, contrastive learning has emerged as a crucial technique for acquiring discriminative representations from graph data. Maximizing...
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Molecular contrastive learning of representations via graph neural networks
Molecular machine learning bears promise for efficient molecular property prediction and drug discovery. However, labelled molecule data can be...
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Intent with knowledge-aware multiview contrastive learning for recommendation
User–item interactions on e-commerce platforms involve various intents, such as browsing and purchasing, which require fine-grained intent...
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Graph-Based Short Text Clustering via Contrastive Learning with Graph Embedding
Clustering is an unsupervised learning technique that helps us quickly classify short texts. It works by effectively capturing the semantic themes of...
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Multi-behavior collaborative contrastive learning for sequential recommendation
Sequential recommendation (SR) predicts the user’s future preferences based on the sequence of interactions. Recently, some methods for SR have...
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Hybrid sampling-based contrastive learning for imbalanced node classification
Imbalanced node classification is a vital task because it widely exists in many real-world applications, such as financial fraud detection,...
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Transformer-based contrastive learning framework for image anomaly detection
Anomaly detection refers to the problem of uncovering patterns in a given data set that do not conform to the expected behavior. Recently, owing to...
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LTACL: long-tail awareness contrastive learning for distantly supervised relation extraction
Distantly supervised relation extraction is an automatically annotating method for large corpora by classifying a bound of sentences with two same...
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Multi-source information contrastive learning collaborative augmented conversational recommender systems
Conversational Recommender Systems (CRS) aim to provide high-quality items to users in fewer conversation rounds using natural language. Despite...
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Contrastive sequential interaction network learning on co-evolving Riemannian spaces
The sequential interaction network usually find itself in a variety of applications, e.g., recommender system. Herein, inferring future interaction...
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Recognition Method with Deep Contrastive Learning and Improved Transformer for 3D Human Motion Pose
Three-dimensional (3D) human pose recognition techniques based on spatial data have gained attention. However, existing models and algorithms fail to...
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Genre: generative multi-turn question answering with contrastive learning for entity–relation extraction
Extractive approaches have been the mainstream paradigm for identifying overlap** entity–relation extraction. However, limited by their inherently...
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Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection
Sarcasm is a prevailing rhetorical device that intentionally uses words that literally meaning opposite the real meaning. Due to this deliberate...
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SimGRL: a simple self-supervised graph representation learning framework via triplets
Recently, graph contrastive learning (GCL) has achieved remarkable performance in graph representation learning. However, existing GCL methods...
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Adversarial Distillation Adaptation Model with Sentiment Contrastive Learning for Zero-Shot Stance Detection
Zero-shot stance detection is both crucial and challenging because it demands detecting the stances of previously unseen targets in the inference...
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A new robust contrastive learning for unsupervised person re-identification
Unsupervised person re-identification (Re-ID) is more substantial than the supervised one because it does not require any labeled samples. Currently,...
