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A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring
Artificial-intelligence methods and machine-learning models have demonstrated their ability
to optimize, model, and automate critical water-and wastewater-treatment applications …
to optimize, model, and automate critical water-and wastewater-treatment applications …
Emergence and causality in complex systems: a survey of causal emergence and related quantitative studies
Emergence and causality are two fundamental concepts for understanding complex
systems. They are interconnected. On one hand, emergence refers to the phenomenon …
systems. They are interconnected. On one hand, emergence refers to the phenomenon …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
Alleviating structural distribution shift in graph anomaly detection
Graph anomaly detection (GAD) is a challenging binary classification problem due to its
different structural distribution between anomalies and normal nodes---abnormal nodes are …
different structural distribution between anomalies and normal nodes---abnormal nodes are …
Out-of-distribution generalization with causal invariant transformations
In real-world applications, it is important and desirable to learn a model that performs well on
out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the …
out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the …
Learning to reweight for generalizable graph neural network
Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing
GNNs' generalization ability will degrade when there exist distribution shifts between testing …
GNNs' generalization ability will degrade when there exist distribution shifts between testing …
Out-of-distribution generalization with causal feature separation
Driven by empirical risk minimization, machine learning algorithm tends to exploit subtle
statistical correlations existing in the training environment for prediction, while the spurious …
statistical correlations existing in the training environment for prediction, while the spurious …
Uncovering main causalities for long-tailed information extraction
Information Extraction (IE) aims to extract structural information from unstructured texts. In
practice, long-tailed distributions caused by the selection bias of a dataset, may lead to …
practice, long-tailed distributions caused by the selection bias of a dataset, may lead to …
A survey on evaluation of out-of-distribution generalization
Machine learning models, while progressively advanced, rely heavily on the IID assumption,
which is often unfulfilled in practice due to inevitable distribution shifts. This renders them …
which is often unfulfilled in practice due to inevitable distribution shifts. This renders them …
A Unified Invariant Learning Framework for Graph Classification
Invariant learning demonstrates substantial potential for enhancing the generalization of
graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize …
graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize …