An efficient, accurate, and interpretable machine learning method for computing probability of failure
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhu, Jacob, Estep, Donald |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A parsimonious, computationally efficient machine learning method for spatial regression
por: Žukovič, Milan, et al.
Publicado: (2023)
por: Žukovič, Milan, et al.
Publicado: (2023)
Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
por: Ke, Luoma, et al.
Publicado: (2025)
por: Ke, Luoma, et al.
Publicado: (2025)
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
por: Xia, Shuyin, et al.
Publicado: (2023)
por: Xia, Shuyin, et al.
Publicado: (2023)
An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer
por: Shahriyar, Omid, et al.
Publicado: (2025)
por: Shahriyar, Omid, et al.
Publicado: (2025)
Spectral methods: crucial for machine learning, natural for quantum computers?
por: Belis, Vasilis, et al.
Publicado: (2026)
por: Belis, Vasilis, et al.
Publicado: (2026)
Are machine learning interpretations reliable? A stability study on global interpretations
por: Gan, Luqin, et al.
Publicado: (2025)
por: Gan, Luqin, et al.
Publicado: (2025)
On the failure of ReLU activation for physics-informed machine learning
por: Rowan, Conor
Publicado: (2025)
por: Rowan, Conor
Publicado: (2025)
Learning accurate and interpretable tree-based models
por: Balcan, Maria-Florina, et al.
Publicado: (2024)
por: Balcan, Maria-Florina, et al.
Publicado: (2024)
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
por: Jacob, Bruno, et al.
Publicado: (2025)
por: Jacob, Bruno, et al.
Publicado: (2025)
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
por: Liu, Junlan, et al.
Publicado: (2024)
por: Liu, Junlan, et al.
Publicado: (2024)
Downscaling human mobility data based on demographic socioeconomic and commuting characteristics using interpretable machine learning methods
por: Jiang, Yuqin, et al.
Publicado: (2025)
por: Jiang, Yuqin, et al.
Publicado: (2025)
Fast, memory-efficient genomic interval tokenizers for modern machine learning
por: LeRoy, Nathan J., et al.
Publicado: (2025)
por: LeRoy, Nathan J., et al.
Publicado: (2025)
META-ANOVA: Screening interactions for interpretable machine learning
por: Choi, Yongchan, et al.
Publicado: (2024)
por: Choi, Yongchan, et al.
Publicado: (2024)
mlr3summary: Concise and interpretable summaries for machine learning models
por: Dandl, Susanne, et al.
Publicado: (2024)
por: Dandl, Susanne, et al.
Publicado: (2024)
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
por: Chowdhury, Prithwijit, et al.
Publicado: (2026)
por: Chowdhury, Prithwijit, et al.
Publicado: (2026)
Recent advances in interpretable machine learning using structure-based protein representations
por: Vecchietti, Luiz Felipe, et al.
Publicado: (2024)
por: Vecchietti, Luiz Felipe, et al.
Publicado: (2024)
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
por: Kempen, Luuk H. E., et al.
Publicado: (2025)
por: Kempen, Luuk H. E., et al.
Publicado: (2025)
Iteratively reweighted kernel machines efficiently learn sparse functions
por: Zhu, Libin, et al.
Publicado: (2025)
por: Zhu, Libin, et al.
Publicado: (2025)
Structured adaptive and random spinners for fast machine learning computations
por: Bojarski, Mariusz, et al.
Publicado: (2016)
por: Bojarski, Mariusz, et al.
Publicado: (2016)
LLM-based feature generation from text for interpretable machine learning
por: Balek, Vojtěch, et al.
Publicado: (2024)
por: Balek, Vojtěch, et al.
Publicado: (2024)
A comparative analysis of machine learning algorithms for predicting probabilities of default
por: Cristescu, Adrian Iulian, et al.
Publicado: (2025)
por: Cristescu, Adrian Iulian, et al.
Publicado: (2025)
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
por: Vlontzou, Maria Eleftheria, et al.
Publicado: (2024)
por: Vlontzou, Maria Eleftheria, et al.
Publicado: (2024)
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects
por: Köhler, David, et al.
Publicado: (2024)
por: Köhler, David, et al.
Publicado: (2024)
Training-efficient density quantum machine learning
por: Coyle, Brian, et al.
Publicado: (2024)
por: Coyle, Brian, et al.
Publicado: (2024)
Leveraging advances in machine learning for the robust classification and interpretation of networks
por: Appaw, Raima Carol, et al.
Publicado: (2024)
por: Appaw, Raima Carol, et al.
Publicado: (2024)
On the definition and importance of interpretability in scientific machine learning
por: Rowan, Conor, et al.
Publicado: (2025)
por: Rowan, Conor, et al.
Publicado: (2025)
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
por: Yuan, Han, et al.
Publicado: (2021)
por: Yuan, Han, et al.
Publicado: (2021)
Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational study
por: Zhao, Zirui, et al.
Publicado: (2025)
por: Zhao, Zirui, et al.
Publicado: (2025)
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
por: Chavlis, Spyridon, et al.
Publicado: (2024)
por: Chavlis, Spyridon, et al.
Publicado: (2024)
Text classification using machine learning methods
por: Oancea, Bogdan
Publicado: (2025)
por: Oancea, Bogdan
Publicado: (2025)
Meshless method stencil evaluation with machine learning
por: Rot, Miha, et al.
Publicado: (2022)
por: Rot, Miha, et al.
Publicado: (2022)
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
por: Bastawrous, Mary V., et al.
Publicado: (2024)
por: Bastawrous, Mary V., et al.
Publicado: (2024)
Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods
por: Barzdajn, Bartosz, et al.
Publicado: (2024)
por: Barzdajn, Bartosz, et al.
Publicado: (2024)
Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance
por: Chen, Dangxing, et al.
Publicado: (2022)
por: Chen, Dangxing, et al.
Publicado: (2022)
Can AI-predicted complexes teach machine learning to compute drug binding affinity?
por: Hsu, Wei-Tse, et al.
Publicado: (2025)
por: Hsu, Wei-Tse, et al.
Publicado: (2025)
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
por: Lei, Yuan-Zheng, et al.
Publicado: (2025)
por: Lei, Yuan-Zheng, et al.
Publicado: (2025)
Sparse learned kernels for interpretable and efficient medical time series processing
por: Chen, Sully F., et al.
Publicado: (2023)
por: Chen, Sully F., et al.
Publicado: (2023)
Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials
por: Bi, Shuyu, et al.
Publicado: (2026)
por: Bi, Shuyu, et al.
Publicado: (2026)
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning
por: Jain, Isha, et al.
Publicado: (2024)
por: Jain, Isha, et al.
Publicado: (2024)
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning
por: Vermariën, Gijs, et al.
Publicado: (2025)
por: Vermariën, Gijs, et al.
Publicado: (2025)
Ejemplares similares
-
A parsimonious, computationally efficient machine learning method for spatial regression
por: Žukovič, Milan, et al.
Publicado: (2023) -
Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
por: Ke, Luoma, et al.
Publicado: (2025) -
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
por: Xia, Shuyin, et al.
Publicado: (2023) -
An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer
por: Shahriyar, Omid, et al.
Publicado: (2025) -
Spectral methods: crucial for machine learning, natural for quantum computers?
por: Belis, Vasilis, et al.
Publicado: (2026)