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Found 9 papers, 2 papers with code
Structured Evaluation of Synthetic Tabular Data
Many metrics exist for evaluating the quality of synthetic tabular data; however, we lack an objective, coherent interpretation of the many metrics.
A Psychological Theory of Explainability
The goal of explainable Artificial Intelligence (XAI) is to generate human-interpretable explanations, but there are no computationally precise theories of how humans interpret AI generated explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Explainable AI for Natural Adversarial Images
Adversarial images highlight how vulnerable modern image classifiers are to perturbations outside of their training set.
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Limited expert time is a key bottleneck in medical imaging.
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee.
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
State-of-the-art deep-learning systems use decision rules that are challenging for humans to model.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Interpretable deep Gaussian processes with moments
We propose interpretable DGP based on approximating DGP as a GP by calculating the exact moments, which additionally identify the heavy-tailed nature of some DGP distributions.
Standing Wave Decomposition Gaussian Process
We propose a Standing Wave Decomposition (SWD) approximation to Gaussian Process regression (GP).
Optimal Cooperative Inference
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners.
