For many applications, analyzing the uncertainty of a machine learning model
is indispensable. While research of uncertainty quantification (UQ) techniques
is very advanced for computer vision applications, UQ methods for
spatio-temporal data are less studied. In this paper, we focus on models for
online handwriting recognition, one particular type of spatio-temporal data.
The data is observed from a sensor-enhanced pen with the goal to classify
written characters. We conduct a broad evaluation of aleatoric (data) and
epistemic (model) UQ based on two prominent techniques for Bayesian inference,
Stochastic Weight Averaging-Gaussian (SWAG) and Deep Ensembles. Next to a
better understanding of the model, UQ techniques can detect out-of-distribution
data and domain shifts when combining right-handed and left-handed writers (an
underrepresented group).

本文主要关注基于贝叶斯推理的在线手写识别模型的不确定性量化，特别是针对时空数据的不确定性量化方法。通过采用 SWAG 和 Deep Ensembles 两种方法，对数据和模型的不确定性进行评估，以更好地了解模型，并在右手和左手写手（一个低比例的群体）组合时检测到超出分布的数据和域漂移等方面取得一定效果。

领域漂移对在线手写识别的时间序列分类的不确定性感知评估

Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift

Convolutional neural networks (CNNs) perform well on problems such as
handwriting recognition and image classification. However, the performance of
the networks is often limited by budget and time constraints, particularly when
trying to train deep networks.
Motivated by the problem of online handwriting recognition, we developed a
CNN for processing spatially-sparse inputs; a character drawn with a one-pixel
wide pen on a high resolution grid looks like a sparse matrix. Taking advantage
of the sparsity allowed us more efficiently to train and test large, deep CNNs.
On the CASIA-OLHWDB1.1 dataset containing 3755 character classes we get a test
error of 3.82%.
Although pictures are not sparse, they can be thought of as sparse by adding
padding. Applying a deep convolutional network using sparsity has resulted in a
substantial reduction in test error on the CIFAR small picture datasets: 6.28%
on CIFAR-10 and 24.30% for CIFAR-100.

通过处理空间稀疏的输入，我们开发了一种 CNN，可更加高效地训练和测试大型深度 CNN，在手写识别和图像分类等问题上取得了良好的性能。该方法在 CASIA-OLHWDB1.1 和 CIFAR-10/CIFAR-100 数据集上实现了较低的测试误差率。