It is desirable to have accurate uncertainty estimation from a single
deterministic forward-pass model, as traditional methods for uncertainty
quantification are computationally expensive. However, this is difficult
because single forward-pass models do not sample weights during inference and
often make assumptions about the target distribution, such as assuming it is
Gaussian. This can be restrictive in regression tasks, where the mean and
standard deviation are inadequate to model the target distribution accurately.
This paper proposes a deep Bayesian quantile regression model that can estimate
the quantiles of a continuous target distribution without the Gaussian
assumption. The proposed method is based on evidential learning, which allows
the model to capture aleatoric and epistemic uncertainty with a single
deterministic forward-pass model. This makes the method efficient and scalable
to large models and datasets. We demonstrate that the proposed method achieves
calibrated uncertainties on non-Gaussian distributions, disentanglement of
aleatoric and epistemic uncertainty, and robustness to out-of-distribution
samples.

该论文提出了一种基于证据学习的深度贝叶斯分位回归模型，能够在没有高斯分布假设的情况下估计连续目标分布的分位数，同时捕捉不确定性和提高模型的可计算性和可扩展性。

深层证据学习用于贝叶斯分位数回归

Deep Evidential Learning for Bayesian Quantile Regression

Modern deep neural networks are prone to being overconfident despite their
drastically improved performance. In ambiguous or even unpredictable real-world
scenarios, this overconfidence can pose a major risk to the safety of
applications. For regression tasks, the regression-by-classification approach
has the potential to alleviate these ambiguities by instead predicting a
discrete probability density over the desired output. However, a density
estimator still tends to be overconfident when trained with the common NLL
loss. To mitigate the overconfidence problem, we propose a loss function,
hinge-Wasserstein, based on the Wasserstein Distance. This loss significantly
improves the quality of both aleatoric and epistemic uncertainty, compared to
previous work. We demonstrate the capabilities of the new loss on a synthetic
dataset, where both types of uncertainty are controlled separately. Moreover,
as a demonstration for real-world scenarios, we evaluate our approach on the
benchmark dataset Horizon Lines in the Wild. On this benchmark, using the
hinge-Wasserstein loss reduces the Area Under Sparsification Error (AUSE) for
horizon parameters slope and offset, by 30.47% and 65.00%, respectively.

本篇论文提出了一种基于 Wasserstein 距离的损失函数 (hinge-Wasserstein)，用于解决深度神经网络训练过程中的置信度过高问题，可以提升模型对两种不确定性的估计能力，并在 Horizon Lines in the Wild 数据集上取得显著的误差减小效果。