Investigating Statistical Inference and Covariate Effects in Shallow Neural Networks

arXiv:2311.08139 · stat.ME, stat.ML · Submitted 2023-11-14 · Read on arXiv

stat.ME, stat.ML

Submitted: 2023-11-14

Updated: 2026-08-31

Terminology

• Interpretability — 6× in this paper · explained in 15 episodes
• Uncertainty Quantification — 5× in this paper · explained in 8 episodes
• Empirical Coverage — 3× in this paper · explained in 2 episodes
• Explainability — 3× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Information Criterion — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Partial Covariate-Effect — 1× in this paper
• Post-Selection — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• On the Robustness of Interpretability Methods
• A Statistical-Modelling Approach to Feedforward Neural Network Model Selection
• Model-Agnostic Interpretability of Machine Learning
• A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay

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