A key problem in computational sustainability is to understand the
distribution of species across landscapes over time. This question gives rise
to challenging large-scale prediction problems since (i) hundreds of species
have to be simultaneously modeled and (ii) the survey data are usually inflated
with zeros due to the absence of species for a large number of sites. The
problem of tackling both issues simultaneously, which we refer to as the
zero-inflated multi-target regression problem, has not been addressed by
previous methods in statistics and machine learning. In this paper, we propose
a novel deep model for the zero-inflated multi-target regression problem. To
this end, we first model the joint distribution of multiple response variables
as a multivariate probit model and then couple the positive outcomes with a
multivariate log-normal distribution. By penalizing the difference between the
two distributions' covariance matrices, a link between both distributions is
established. The whole model is cast as an end-to-end learning framework and we
provide an efficient learning algorithm for our model that can be fully
implemented on GPUs. We show that our model outperforms the existing
state-of-the-art baselines on two challenging real-world species distribution
datasets concerning bird and fish populations.

本文提出了一种深度模型来解决计算可持续性中的物种分布问题，其中有大量的零填充数据，并且需要同时对数百个物种进行建模，该模型被称为 “零膨胀多目标回归问题”。通过将整个模型作为端到端的学习框架，我们提供了一种有效的学习算法，并在两个实际物种分布的数据集上展示了该模型比现有的最先进基线模型表现更好，其核心是将多响应变量的联合分布建模为多元 Probit 模型。