Uncertainty Quantification

Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation
In this work, we investigate the suitability of PEFT methods for subspace Bayesian inference in large-scale Transformer-based vision models.
Richard D. Paul*, Alessio Quercia*, Vincent Fortuin, Katharina Nöh, Hanno Scharr
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Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation