We study the task of spatio-temporal extrapolation that generates data at
target locations from surrounding contexts in a graph. This task is crucial as
sensors that collect data are sparsely deployed, resulting in a lack of
fine-grained information due to high deployment and maintenance costs. Existing
methods either use learning-based models like Neural Networks or statistical
approaches like Gaussian Processes for this task. However, the former lacks
uncertainty estimates and the latter fails to capture complex spatial and
temporal correlations effectively. To address these issues, we propose
Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model
which commands these capabilities simultaneously. Specifically, we first learn
deterministic spatio-temporal representations by stacking layers of causal
convolutions and cross-set graph neural networks. Then, we learn latent
variables for target locations through vertical latent state transitions along
layers and obtain extrapolations. Importantly during the transitions, we
propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that
aggregates contexts considering uncertainties in context data and graph
structure. Extensive experiments show that STGNP has desirable properties such
as uncertainty estimates and strong learning capabilities, and achieves
state-of-the-art results by a clear margin.

本文研究了在图形上从周围环境生成目标位置数据的时空外推任务，提出了具有不确定性估计和强大学习能力的空间 - 时间图神经过程模型，并使用贝叶斯聚合算法在转移过程中对上下文数据和图形结构进行了考虑。

基于图神经网络的时空外推技术

Graph Neural Processes for Spatio-Temporal Extrapolation

Neural processes (NPs) have brought the representation power of parametric
deep neural networks and the reliable uncertainty estimation of non-parametric
Gaussian processes together. Although recent development of NPs has shown
success in both regression and classification, how to adapt NPs to multimodal
data has not be carefully studied. For the first time, we propose a new model
of NP family for multimodal uncertainty estimation, namely Multimodal Neural
Processes. In a holistic and principled way, we develop a dynamic context
memory updated by the classification error, a multimodal Bayesian aggregation
mechanism to aggregate multimodal representations, and a new attention
mechanism for calibrated predictions. In extensive empirical evaluation, our
method achieves the state-of-the-art multimodal uncertainty estimation
performance, showing its appealing ability of being robust against noisy
samples and reliable in out-of-domain detection.

提出了一种新的多模态神经过程模型，称为 Multimodal Neural Processes，该模型通过动态上下文存储、多模态贝叶斯聚合机制和新的注意力机制来进行多模态不确定性估计，实验结果表明其在多模态不确定性估计方面表现优异。

多模态神经过程用于不确定性估计

Multimodal Neural Processes for Uncertainty Estimation

Crowdsourcing systems commonly face the problem of aggregating multiple
judgments provided by potentially unreliable workers. In addition, several
aspects of the design of efficient crowdsourcing processes, such as defining
worker's bonuses, fair prices and time limits of the tasks, involve knowledge
of the likely duration of the task at hand. Bringing this together, in this
work we introduce a new time--sensitive Bayesian aggregation method that
simultaneously estimates a task's duration and obtains reliable aggregations of
crowdsourced judgments. Our method, called BCCTime, builds on the key insight
that the time taken by a worker to perform a task is an important indicator of
the likely quality of the produced judgment. To capture this, BCCTime uses
latent variables to represent the uncertainty about the workers' completion
time, the tasks' duration and the workers' accuracy. To relate the quality of a
judgment to the time a worker spends on a task, our model assumes that each
task is completed within a latent time window within which all workers with a
propensity to genuinely attempt the labelling task (i.e., no spammers) are
expected to submit their judgments. In contrast, workers with a lower
propensity to valid labeling, such as spammers, bots or lazy labelers, are
assumed to perform tasks considerably faster or slower than the time required
by normal workers. Specifically, we use efficient message-passing Bayesian
inference to learn approximate posterior probabilities of (i) the confusion
matrix of each worker, (ii) the propensity to valid labeling of each worker,
(iii) the unbiased duration of each task and (iv) the true label of each task.
Using two real-world public datasets for entity linking tasks, we show that
BCCTime produces up to 11% more accurate classifications and up to 100% more
informative estimates of a task's duration compared to state-of-the-art
methods.

本文介绍了一种新的时间敏感的贝叶斯聚合方法，称为 BCCTime，该方法同时估计任务的持续时间并获得可靠的聚合众包判断，该方法使用潜在变量表示关于工人完成时间、任务持续时间和工人准确性的不确定性，通过高效的信息传递贝叶斯推理来学习关于每个工人和任务的后验概率，并与现有方法相比产生更准确的分类以及更多的任务持续时间估计。