Epistemic Neural Network (ENN)
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A Epistemic Neural Network (ENN) is a neural network that is designed to quantify and model epistemic uncertainty.
- Context:
- It can (typically) use Bayesian methods or other techniques to represent uncertainty in the model's parameters, thereby allowing for a probabilistic interpretation of model weights rather than fixed values.
- It can (often) provide outputs that include both the prediction and a measure of uncertainty, which can guide decision-making by indicating when the model's predictions are reliable and when they should be treated with caution.
- It can estimate the uncertainty associated with its predictions, which is crucial for applications where understanding the confidence level of a prediction is as important as the prediction itself (e.g., decision-making processes, medical diagnosis, and autonomous driving).
- It can be particularly useful in active learning and exploration, as it can prioritize data points for which the model is uncertain, thereby enabling more efficient learning by focusing on gathering information that most improves the model.
- It can enhance safety in critical systems by assessing risk and making informed decisions through quantified uncertainty, which is especially important in areas where mistakes can have severe consequences.
- ...
- Example(s):
- A Bayesian Neural Network that applies Bayes' theorem to its weights to quantify uncertainty in predictions.
- One used in autonomous driving to assess the safety of decisions under uncertain conditions.
- ...
- Counter-Example(s):
- A Standard Neural Network that provides predictions without any measure of uncertainty.
- A Decision Tree Classifier that does not quantify epistemic or aleatoric uncertainty.
- See: Bayesian methods, Aleatoric uncertainty, Active learning, Safety-critical systems, Personalized recommendation systems.
References
2024
- (Dwaracherla et al., 2024) ⇒ Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao, and Benjamin Van Roy. (2024). “Efficient Exploration for LLMs.” doi:10.48550/arXiv.2402.00396