基于深度双泊松网络的完全异方差计数回归
Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
AI总结：
针对计数回归领域缺乏类似连续回归的异方差不确定性建模方法的问题，提出输出双泊松分布参数的深度双泊松网络DDPN，可灵活表征计数数据的偶然不确定性并提升集成后的认知不确定性估计，在多数据集上的准确率、校准度和分布外检测均达最优水平。
摘要：
能够实现准确的、以输入为条件的不确定性表征的神经网络对现实世界AI系统至关重要。高斯网络深度集成（deep ensembles）已被证明在连续回归任务中极具成效，因为它们能通过无约束的异方差方差灵活表征偶然不确定性（aleatoric uncertainty），进而实现准确的认知不确定性（epistemic uncertainty）估计。然而，尽管计数回归有诸多重要应用，目前尚无类似的方法。为填补这一空白，我们提出深度双泊松网络（Deep Double Poisson Network, DDPN），这是一种新型神经离散计数回归模型，可输出双泊松分布的参数，能为计数数据提供任意高低的预测偶然不确定性，并在集成时提升认知不确定性估计效果。我们通过可学习的损失衰减形式化并证明了DDPN具备与异方差高斯模型相似的鲁棒回归特性，并引入了一种简单的损失修正方法来控制这一行为。在多个不同数据集上的实验表明，DDPN在准确率、校准度和分布外检测方面均优于现有基线，确立了深度计数回归领域的新的最优水平（state-of-the-art）。
英文摘要：
Neural networks capable of accurate, input-conditional uncertainty representation are essential for real-world AI systems. Deep ensembles of Gaussian networks have proven highly effective for continuous regression due to their ability to flexibly represent aleatoric uncertainty via unrestricted heteroscedastic variance, which in turn enables accurate epistemic uncertainty estimation. However, no analogous approach exists for count regression, despite many important applications. To address this gap, we propose the Deep Double Poisson Network (DDPN), a novel neural discrete count regression model that outputs the parameters of the Double Poisson distribution, enabling arbitrarily high or low predictive aleatoric uncertainty for count data and improving epistemic uncertainty estimation when ensembled. We formalize and prove that DDPN exhibits robust regression properties similar to heteroscedastic Gaussian models via learnable loss attenuation, and introduce a simple loss modification to control this behavior. Experiments on diverse datasets demonstrate that DDPN outperforms current baselines in accuracy, calibration, and out-of-distribution detection, establishing a new state-of-the-art in deep count regression.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
