AIMC Topic: Uncertainty

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Uncertainty-Informed Screening for Safer Solvents Used in the Synthesis of Perovskites via Language Models.

Journal of chemical information and modeling Jul 22, 2025
Automated data curation for niche scientific topics, where data quality and contextual accuracy are paramount, poses significant challenges. Bidirectional contextual models such as BERT and ELMo excel in contextual understanding and determinism. Howe...
Solvents Calcium Compounds Uncertainty Oxides Titanium
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Explainable Graph Neural Networks in Chemistry: Combining Attribution and Uncertainty Quantification.

Journal of chemical information and modeling Jul 17, 2025
Graph Neural Networks (GNNs) are powerful tools for predicting chemical properties, but their black-box nature can limit trust and utility. Explainability through feature attribution and awareness of prediction uncertainty are critical for practical ...
Uncertainty Graph Neural Networks Solubility Molecular Weight Neural Networks, Computer
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Uncertainty aware domain incremental learning for cross domain depression detection.

Scientific reports Jul 14, 2025
Deep learning techniques have demonstrated significant promise for detecting Major Depressive Disorder (MDD) from textual data but they still face limitations in real-world scenarios. Specifically, given the limited data availability, some efforts ha...
Deep Learning Depression Uncertainty Humans Depressive Disorder, Major
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Towards more reliable prostate cancer detection: Incorporating clinical data and uncertainty in MRI deep learning.

Computers in biology and medicine Jun 11, 2025
Prostate cancer (PCa) is one of the most common cancers among men, and artificial intelligence (AI) is emerging as a promising tool to enhance its diagnosis. This work proposes a classification approach for PCa cases using deep learning techniques. W...
Male Middle Aged Humans Aged Uncertainty Prostatic Neoplasms Image Interpretation, Computer-Assisted Deep Learning Magnetic Resonance Imaging
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Aggregating soft labels from crowd annotations improves uncertainty estimation under distribution shift.

PloS one Jun 9, 2025
Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may not be reliable. Recent work has demonstrated that learning from a distribution over labels acquired f...
Humans Crowdsourcing Uncertainty Machine Learning Algorithms
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SASWISE-UE: Segmentation and synthesis with interpretable scalable ensembles for uncertainty estimation.

Computers in biology and medicine Jun 2, 2025
This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to...
Magnetic Resonance Imaging Uncertainty Deep Learning Image Processing, Computer-Assisted Humans Algorithms Tomography, X-Ray Computed
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Uncertainty Quantification and Temperature Scaling Calibration for Protein-RNA Binding Site Prediction.

Journal of chemical information and modeling Jun 2, 2025
The black-box nature of deep learning has increasingly drawn attention to the reliability and uncertainty of predictive models. Currently, several uncertainty quantification (UQ) methods have been proposed and successfully applied in the fields of mo...
Neural Networks, Computer Temperature Protein Binding RNA-Binding Proteins Binding Sites Uncertainty RNA Calibration
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VKAD: A novel fault detection and isolation model for uncertainty-aware industrial processes.

Neural networks : the official journal of the International Neural Network Society May 22, 2025
Fault detection and isolation (FDI) are essential for effective monitoring of industrial processes. Modern industrial processes involve dynamic systems characterized by complex, high-dimensional nonlinearities, posing significant challenges for accur...
Industry Uncertainty Neural Networks, Computer Nonlinear Dynamics
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A controlled trial examining large Language model conformity in psychiatric assessment using the Asch paradigm.

BMC psychiatry May 12, 2025
BACKGROUND: Despite significant advances in AI-driven medical diagnostics, the integration of large language models (LLMs) into psychiatric practice presents unique challenges. While LLMs demonstrate high accuracy in controlled settings, their perfor...
Uncertainty Adult Language Brain Neoplasms Child Male Female Large Language Models Adolescent Humans Mental Disorders
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Probabilistic design space exploration and optimization via bayesian approach for a fluid bed drying process.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences May 3, 2025
The concept of Design Space (DS), delineated as a region of investigated variables aimed at maintaining product quality, was introduced in the International Conference on Harmonisation (ICH) Q8 as a framework to direct pharmaceutical development. How...
Desiccation Uncertainty Reproducibility of Results Bayes Theorem Models, Statistical Technology, Pharmaceutical
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