A new survey paper published on arXiv details methods for uncertainty quantification in deep learning, focusing on techniques relevant for trustworthy AI in safety-critical applications. The paper categorizes approaches into Bayesian neural networks, Monte Carlo Dropout, deep ensembles, and single-pass methods. It also reviews measures for summarizing uncertainty and discusses applications in large language models, highlighting open research directions. AI
IMPACT Provides a structured overview of methods to improve the reliability and trustworthiness of deep learning models in critical applications.
RANK_REASON The item is a survey paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bayesian Neural Networks
- Conformal prediction
- Deep Ensembles
- deep learning
- Hugging Face
- large-language models
- Monte Carlo Dropout
- uncertainty quantification
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