An Accurate and Interpretable Framework for Trustworthy Process Monitoring
Explore this paper's citation graph
- Type
- preprint
- Published
- 2023-02-21
- Cited by
- 35
- References
- 72
- Access
- Open access
- OpenAlex
- https://openalex.org/W4321592975
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:263186083
Keywords
Interpretability, Computer science, Benchmark (surveying), Process (computing), Spurious relationship
References
- A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting
- Estimation of infiltration and surface run-off characteristics of radionuclides from gamma dose rate change after rain
- Increased environmental gamma-ray dose rate during precipitation: a strong correlation with contributing air mass.
- Hybrid identification of nuclear power plant transients with artificial neural networks
- Embodied energy analysis for coal-based power generation system-highlighting the role of indirect energy cost
- Deep Learning on Graphs
- Statistical analysis of a time series relevant to passive systems of nuclear power plants
- Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant
- Going nuclear for climate mitigation: An analysis of the cost effectiveness of preserving existing U.S. nuclear power plants as a carbon avoidance strategy
- NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion
- Adaptive Power Transformer Lifetime Predictions Through Machine Learning and Uncertainty Modeling in Nuclear Power Plants
- Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network
- Establishing the rules for building trustworthy AI
- Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
- Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
- Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network
- Pressurizer Water Level Reconstruction for Nuclear Power Plant Based on GRU
- Determination of spent nuclear fuel parameters using modelled signatures from non-destructive assay and Random Forest regression
- Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
- CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
Cited by
- Hidformer: Hierarchical dual-tower transformer using multi-scale mergence for long-term time series forecasting
- Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting
- Denoising Diffusion Straightforward Models for Energy Conversion Monitoring Data Imputation
- SPOT-I: Similarity Preserved Optimal Transport for Industrial IoT Data Imputation
- Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
- LSPT-D: Local Similarity Preserved Transport for Direct Industrial Data Imputation
- TMoE-P: Toward the Pareto Optimum for Multivariate Soft Sensors
- PPGF: Probability Pattern-Guided Time Series Forecasting
- AKGNN: When Adaptive Graph Neural Network Meets Kolmogorov–Arnold Network for Industrial Soft Sensors
- E^2AG: Entropy-Regularized Ensemble Adaptive Graph for Industrial Soft Sensor Modeling
- Post Constraint and Correction: A Plug-and-Play Module for Boosting the Performance of Deep Learning Based Weather Multivariate Time Series Forecasting
- DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
- Robust Missing Value Imputation With Proximal Optimal Transport for Low-Quality IIoT Data
- K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
- Blending Data and Knowledge for Process Industrial Modeling Under Riemannian Preconditioned Bayesian Framework
- ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
- Bidirectional Temporal-Aware Modeling with Multi-Scale Mixture-of-Experts for Multivariate Time Series Forecasting
- Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
- Aurora: Towards Universal Generative Multimodal Time Series Forecasting
- Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective
Related papers
- ML Interpretability: Simple Isn't Easy
- When consumers need more interpretability of artificial intelligence (AI) recommendations? The effect of decision-making domains
- Effects of mixed sample data augmentation on interpretability of neural networks
- Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately
- Ant Colony Optimization Algorithm for Interpretable Bayesian Classifiers Combination: Application to Medical Predictions
- Interpretability in HealthCare A Comparative Study of Local Machine Learning Interpretability Techniques
- Interpretable machine learning for building energy management: A state-of-the-art review