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Found 7 papers, 3 papers with code
Interpretable Architecture Neural Networks for Function Visualization
Existing visualization tools do not allow one to visualize the effects of all the input variables simultaneously.
Fully Bayesian inference for latent variable Gaussian process models
However, this plug-in approach will not account for uncertainty in estimation of the LVs, which can be significant especially with limited training data.
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.
Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors
Supervised learning models are one of the most fundamental classes of models.
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors
Computer simulations often involve both qualitative and numerical inputs.
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
When fitting black box supervised learning models (e. g., complex trees, neural networks, boosted trees, random forests, nearest neighbors, local kernel-weighted methods, etc.
Methodology
Local Gaussian process approximation for large computer experiments
We provide a new approach to approximate emulation of large computer experiments.
Methodology Computation
