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Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice
As scientists start to adopt machine learning (ML) as one research tool, the security of ML and the knowledge generated become a concern. In this...
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Uncertainty involved drag divergence characteristic predicting method based on VAE
Effective access to obtain the drag divergence characteristic of an airfoil is crucial for improving the economy, safety, and comfort of the...
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Controllable image generation based on causal representation learning
Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as...
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Logit prototype learning with active multimodal representation for robust open-set recognition
Robust open-set recognition (OSR) performance has become a prerequisite for pattern recognition systems in real-world applications. However, the...
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Adversarial data splitting for domain generalization
Domain generalization aims to learn a model that is generalizable to an unseen target domain, which is a fundamental and challenging task in machine...
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Develo** an advanced neural network and physics solver coupled framework for accelerating flow field simulations
Computational fluid dynamics simulation accounts for a large number of workloads in the numerical design optimization of aerodynamics problems. In...
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Granger causal representation learning for groups of time series
Discovering causality from multivariate time series is an important but challenging problem. Most existing methods focus on estimating the Granger...
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Wire rope damage detection based on a uniform-complementary binary pattern with exponentially weighted guide image filtering
In response to the problem of unclear texture structure in steel wire rope images caused by complex and uncertain lighting conditions, resulting in...
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Difficulty-aware prior-guided hierarchical network for adaptive segmentation of breast tumors
Breast tumor segmentation is vital to tumor detection at the early stages. Deep learning methods are typically used in automatic tumor segmentation...
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On the principles of Parsimony and Self-consistency for the emergence of intelligence
Ten years into the revival of deep networks and artificial intelligence, we propose a theoretical framework that sheds light on understanding deep...
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Multi-instance partial-label learning: towards exploiting dual inexact supervision
Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label...
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Abductive subconcept learning
Bridging neural network learning and symbolic reasoning is crucial for strong AI. Few pioneering studies have made some progress on logical reasoning...
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Automatic error function learning with interpretable compositional networks
In Constraint Programming, constraints are usually represented as predicates allowing or forbidding combinations of values. However, some algorithms...
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Learning preference representations based on Choquet integrals for multicriteria decision making
This paper concerns preference elicitation and learning of decision models in the context of multicriteria decision making. We propose an approach to...
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Reinforcement learning-based cost-sensitive classifier for imbalanced fault classification
Fault classification plays a crucial role in the industrial process monitoring domain. In the datasets collected from real-life industrial processes,...
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Learning the external and internal priors for multispectral and hyperspectral image fusion
Recently, multispectral image (MSI) and hyperspectral image (HSI) fusion has been a popular topic in high-resolution HSI acquisition. This fusion...
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Into the unknown: active monitoring of neural networks (extended version)
Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy...
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Domain adversarial neural networks for domain generalization: when it works and how to improve
Theoretically, domain adaptation is a well-researched problem. Further, this theory has been well-used in practice. In particular, we note the bound...
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A personality-guided affective brain—computer interface for implementation of emotional intelligence in machines
Affective brain—computer interfaces have become an increasingly important topic to achieve emotional intelligence in human—machine collaboration....
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Class attention network for image recognition
Visual attention has become a popular and widely used component for image recognition. Although various attention-based methods have been proposed...