Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data. CNPs scale to large datasets and train with ease. Due to these features, CNPs appear well-suited to tasks from environmental sciences or healthcare. Unfortunately, CNPs do not produce correlated predictions, making them fundamentally inappropriate for many estimation and decision making tasks. Predicting heat waves or floods, for example, requires modelling dependencies in temperature or precipitation over time and space. Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive. What is needed is an approach which provides dependent predictions, but is simple to train and computationally tractable. In this work, we present a new class of Neural Process models that make correlated predictions and support exact maximum likelihood training that is simple and scalable. We extend the proposed models by using invertible output transformations, to capture non-Gaussian output distributions. Our models can be used in downstream estimation tasks which require dependent function samples. By accounting for output dependencies, our models show improved predictive performance on a range of experiments with synthetic and real data.

翻译：有条件神经过程(CNPs;Garnelo等人,2018年a)是元学习模型,利用深层学习的灵活性来提出经适当校准的预测,自然处理离网和缺失的数据。由于这些特点,国家NPs的规模与大型数据集相比,并轻松地进行培训。由于这些特点,国家NP似乎完全适合环境科学或保健任务。不幸的是,国家NPs并不产生相关的预测,使其根本不适合许多估算和决策任务。预测热浪或洪水,例如,需要模拟温度或降水量在时间和空间上的依赖性。现有的方法,模型产出依赖性,如神经过程(NPs;Garnelo等人,2018b)或全面ConvNation(Bruinsma等人,2021年),要么很复杂,要么适合培训费用过高。需要一种方法,提供依赖性的预测,但很容易进行培训和计算。在这项工作中,我们展示了一种新的神经过程模型,在进行关联性预测,并支持精确性数据转换时,我们使用的是简单、不依赖性的产出分配。