Saved in:
| Main Authors: | Jiang, Yuqin, Popov, Andrey A., Duan, Tianle, Li, Qingchun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21703 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Recent advances in interpretable machine learning using structure-based protein representations
by: Vecchietti, Luiz Felipe, et al.
Published: (2024)
by: Vecchietti, Luiz Felipe, et al.
Published: (2024)
An efficient, accurate, and interpretable machine learning method for computing probability of failure
by: Zhu, Jacob, et al.
Published: (2026)
by: Zhu, Jacob, et al.
Published: (2026)
Are machine learning interpretations reliable? A stability study on global interpretations
by: Gan, Luqin, et al.
Published: (2025)
by: Gan, Luqin, et al.
Published: (2025)
LLM-based feature generation from text for interpretable machine learning
by: Balek, Vojtěch, et al.
Published: (2024)
by: Balek, Vojtěch, et al.
Published: (2024)
Physics-informed extreme learning machine for Terzaghi consolidation problems and interpretation of coefficient of consolidation based on CPTu data
by: Yang, He, et al.
Published: (2025)
by: Yang, He, et al.
Published: (2025)
Text classification using machine learning methods
by: Oancea, Bogdan
Published: (2025)
by: Oancea, Bogdan
Published: (2025)
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
by: Xie, Feng, et al.
Published: (2021)
by: Xie, Feng, et al.
Published: (2021)
META-ANOVA: Screening interactions for interpretable machine learning
by: Choi, Yongchan, et al.
Published: (2024)
by: Choi, Yongchan, et al.
Published: (2024)
Stochastic parameter reduced-order model based on hybrid machine learning approaches
by: Fang, Cheng, et al.
Published: (2024)
by: Fang, Cheng, et al.
Published: (2024)
Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
by: Bastawrous, Mary V., et al.
Published: (2024)
by: Bastawrous, Mary V., et al.
Published: (2024)
mlr3summary: Concise and interpretable summaries for machine learning models
by: Dandl, Susanne, et al.
Published: (2024)
by: Dandl, Susanne, et al.
Published: (2024)
Generative Diffusion-based Downscaling for Climate
by: Watt, Robbie A., et al.
Published: (2024)
by: Watt, Robbie A., et al.
Published: (2024)
Integrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning
by: Xu, Rui, et al.
Published: (2025)
by: Xu, Rui, et al.
Published: (2025)
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
by: Yuan, Han, et al.
Published: (2021)
by: Yuan, Han, et al.
Published: (2021)
Leveraging advances in machine learning for the robust classification and interpretation of networks
by: Appaw, Raima Carol, et al.
Published: (2024)
by: Appaw, Raima Carol, et al.
Published: (2024)
Deep learning outperforms traditional machine learning methods in predicting childhood malnutrition: evidence from survey data
by: Bastola, Deepak, et al.
Published: (2026)
by: Bastola, Deepak, et al.
Published: (2026)
Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach
by: Jabbar, Zain, et al.
Published: (2026)
by: Jabbar, Zain, et al.
Published: (2026)
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
by: Chowdhury, Prithwijit, et al.
Published: (2026)
by: Chowdhury, Prithwijit, et al.
Published: (2026)
Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
by: Ke, Luoma, et al.
Published: (2025)
by: Ke, Luoma, et al.
Published: (2025)
Causality in the human niche: lessons for machine learning
by: Lange, Richard D., et al.
Published: (2025)
by: Lange, Richard D., et al.
Published: (2025)
Explanatory machine learning for sequential human teaching
by: Ai, Lun, et al.
Published: (2022)
by: Ai, Lun, et al.
Published: (2022)
Using machine learning method for variable star classification using the TESS Sectors 1-57 data
by: Wang, Li-Heng, et al.
Published: (2025)
by: Wang, Li-Heng, et al.
Published: (2025)
Foreclassing: A new machine learning perspective on human decision making with temporal data
by: Coulson, Daniel Andrew, et al.
Published: (2025)
by: Coulson, Daniel Andrew, et al.
Published: (2025)
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
by: Pan, Liangrui, et al.
Published: (2023)
by: Pan, Liangrui, et al.
Published: (2023)
Extending machine learning model for implicit solvation to free energy calculations
by: Dey, Rishabh, et al.
Published: (2025)
by: Dey, Rishabh, et al.
Published: (2025)
Extrapolation to infinite model space of no-core shell model calculations using machine learning
by: Mazur, Aleksandr, et al.
Published: (2025)
by: Mazur, Aleksandr, et al.
Published: (2025)
Compositional learning of functions in humans and machines
by: Zhou, Yanli, et al.
Published: (2024)
by: Zhou, Yanli, et al.
Published: (2024)
Enhancing lithological interpretation from petrophysical well log of IODP expedition 390/393 using machine learning
by: Sahu, Raj, et al.
Published: (2025)
by: Sahu, Raj, et al.
Published: (2025)
On the definition and importance of interpretability in scientific machine learning
by: Rowan, Conor, et al.
Published: (2025)
by: Rowan, Conor, et al.
Published: (2025)
Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation
by: Tanashkin, Alexey S., et al.
Published: (2025)
by: Tanashkin, Alexey S., et al.
Published: (2025)
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
by: Vlontzou, Maria Eleftheria, et al.
Published: (2024)
by: Vlontzou, Maria Eleftheria, et al.
Published: (2024)
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects
by: Köhler, David, et al.
Published: (2024)
by: Köhler, David, et al.
Published: (2024)
The Ensemble Epanechnikov Mixture Filter
by: Popov, Andrey A., et al.
Published: (2024)
by: Popov, Andrey A., et al.
Published: (2024)
Sea wave data reconstruction using micro-seismic measurements and machine learning methods
by: Iafolla, Lorenzo, et al.
Published: (2024)
by: Iafolla, Lorenzo, et al.
Published: (2024)
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
by: Madamidola, Oladipo A., et al.
Published: (2024)
by: Madamidola, Oladipo A., et al.
Published: (2024)
Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)
by: Riley, Richard D, et al.
Published: (2022)
Meshless method stencil evaluation with machine learning
by: Rot, Miha, et al.
Published: (2022)
by: Rot, Miha, et al.
Published: (2022)
AIDetx: a compression-based method for identification of machine-learning generated text
by: Almeida, Leonardo, et al.
Published: (2024)
by: Almeida, Leonardo, et al.
Published: (2024)
SLIDE: A machine-learning based method for forced dynamic response estimation of multibody systems
by: Manzl, Peter, et al.
Published: (2024)
by: Manzl, Peter, et al.
Published: (2024)
Physics-based machine learning framework for predicting NOx emissions from compression ignition engines using on-board diagnostics data
by: Selvam, Harish Panneer, et al.
Published: (2025)
by: Selvam, Harish Panneer, et al.
Published: (2025)
Similar Items
-
Recent advances in interpretable machine learning using structure-based protein representations
by: Vecchietti, Luiz Felipe, et al.
Published: (2024) -
An efficient, accurate, and interpretable machine learning method for computing probability of failure
by: Zhu, Jacob, et al.
Published: (2026) -
Are machine learning interpretations reliable? A stability study on global interpretations
by: Gan, Luqin, et al.
Published: (2025) -
LLM-based feature generation from text for interpretable machine learning
by: Balek, Vojtěch, et al.
Published: (2024) -
Physics-informed extreme learning machine for Terzaghi consolidation problems and interpretation of coefficient of consolidation based on CPTu data
by: Yang, He, et al.
Published: (2025)