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De-confounding representation learning for counterfactual inference on continuous treatment via generative adversarial network
Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are...
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Enhancing racism classification: an automatic multilingual data annotation system using self-training and CNN
Accurate racism classification is crucial on social media, where racist and discriminatory content can harm individuals and society. Automated racism...
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Gradient-based explanation for non-linear non-parametric dimensionality reduction
Dimensionality reduction (DR) is a popular technique that shows great results to analyze high-dimensional data. Generally, DR is used to produce...
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Explainable decomposition of nested dense subgraphs
Discovering dense regions in a graph is a popular tool for analyzing graphs. While useful, analyzing such decompositions may be difficult without...
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Extract Implicit Semantic Friends and Their Influences from Bipartite Network for Social Recommendation
Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that...
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Negative-sample-free knowledge graph embedding
Recently, knowledge graphs (KGs) have been shown to benefit many machine learning applications in multiple domains (e.g. self-driving, agriculture,...
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Knowledge graph embedding closed under composition
Knowledge Graph Embedding (KGE) has attracted increasing attention. Relation patterns, such as symmetry and inversion, have received considerable...
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Towards effective urban region-of-interest demand modeling via graph representation learning
Identifying the region’s functionalities and what the specific Point-of-Interest (POI) needs is essential for effective urban planning. However, due...
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Multi-view Heterogeneous Graph Neural Networks for Node Classification
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been...
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Randomnet: clustering time series using untrained deep neural networks
Neural networks are widely used in machine learning and data mining. Typically, these networks need to be trained, implying the adjustment of weights...
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Series2vec: similarity-based self-supervised representation learning for time series classification
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of...
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Robust explainer recommendation for time series classification
Time series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition,...
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GeoRF: a geospatial random forest
The geospatial domain increasingly relies on data-driven methodologies to extract actionable insights from the growing volume of available data....
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Graph-Enhanced Prompt Learning for Personalized Review Generation
Personalized review generation is significant for e-commerce applications, such as providing explainable recommendation and assisting the composition...
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Modelling event sequence data by type-wise neural point process
Event sequence data widely exists in real life, where each event is typically represented as a tuple, event type and occurrence time. Recently,...
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Channel-Enhanced Contrastive Cross-Domain Sequential Recommendation
Sequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness...
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The impact of variable ordering on Bayesian network structure learning
Causal Bayesian Networks (CBNs) provide an important tool for reasoning under uncertainty with potential application to many complex causal systems....
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Erdos: A Novel Blockchain Consensus Algorithm with Equitable Node Selection and Deterministic Block Finalization
The introduction of blockchain technology has brought about significant transformation in the realm of digital transactions, providing a secure and...
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Uplift modeling with quasi-loss-functions
Uplift modeling, also referred to as heterogeneous treatment effect estimation, is a machine learning technique utilized in marketing for estimating...