AIMC Topic: Probability

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Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland.

Journal of environmental management Mar 24, 2025
For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very fe...
Soil Farms Machine Learning Soil Pollutants Probability Czech Republic Agriculture Environmental Monitoring
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Deep one-class probability learning for end-to-end image classification.

Neural networks : the official journal of the International Neural Network Society Jan 27, 2025
One-class learning has many application potentials in novelty, anomaly, and outlier detection systems. It aims to distinguish both positive and negative samples with a model trained via only positive samples or one-class annotated samples. With the d...
Humans Neural Networks, Computer Algorithms Probability Probability Learning Deep Learning Image Processing, Computer-Assisted
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CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks.

Neural networks : the official journal of the International Neural Network Society Jan 27, 2025
Effective uncertainty estimation is becoming increasingly attractive for enhancing the reliability of neural networks. This work presents a novel approach, termed Credal-Set Interval Neural Networks (CreINNs), for classification. CreINNs retain the f...
Uncertainty Humans Neural Networks, Computer Bayes Theorem Algorithms Probability
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On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach.

Neural networks : the official journal of the International Neural Network Society Dec 24, 2024
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr...
Algorithms Humans Probability Neural Networks, Computer Graph Neural Networks
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Probabilistic-sampling-based asynchronous control for semi-Markov jumping neural networks with reaction-diffusion terms.

Neural networks : the official journal of the International Neural Network Society Dec 24, 2024
This paper investigates the probabilistic-sampling-based asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs). Aiming at mitigating the drawback of the well-known fixed-sampling control law, a more general probabi...
Markov Chains Stochastic Processes Computer Simulation Probability Neural Networks, Computer Algorithms
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Unifying invariant and variant features for graph out-of-distribution via probability of necessity and sufficiency.

Neural networks : the official journal of the International Neural Network Society Dec 20, 2024
Graph Out-of-Distribution (OOD), requiring that models trained on biased data generalize to the unseen test data, has considerable real-world applications. One of the most mainstream methods is to extract the invariant subgraph by aligning the origin...
Neural Networks, Computer Machine Learning Humans Algorithms Probability
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A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults.

Computers, informatics, nursing : CIN Dec 1, 2024
Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier i...
East Asian People Bayes Theorem Accidental Falls Logistic Models Probability Independent Living Aged, 80 and over Male Risk Assessment China Risk Factors Aged Machine Learning Female Humans
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Justifying Our Credences in the Trustworthiness of AI Systems: A Reliabilistic Approach.

Science and engineering ethics Nov 21, 2024
We address an open problem in the philosophy of artificial intelligence (AI): how to justify the epistemic attitudes we have towards the trustworthiness of AI systems. The problem is important, as providing reasons to believe that AI systems are wort...
Knowledge Humans Philosophy Artificial Intelligence Probability Trust Attitude
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Rethinking density ratio estimation based hyper-parameter optimization.

Neural networks : the official journal of the International Neural Network Society Nov 20, 2024
Hyper-parameter optimization (HPO) aims to improve the performance of machine learning algorithms by identifying appropriate hyper-parameters. By converting the computation of expected improvement into density-ratio estimation problems, existing work...
Probability Machine Learning Humans Algorithms Neural Networks, Computer
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Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling.

Journal of environmental management Oct 30, 2024
Predicting the probability that a given location will be burnt by a wildfire is an important part of understanding the risk that wildfires pose and how our management actions (e.g., prescribed burning) can reduce this risk. Existing methods to quanti...
Models, Theoretical Neural Networks, Computer Fires Machine Learning Probability Wildfires
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