The field of general time series analysis has recently begun to explore
unified modeling, where a common architectural backbone can be retrained on a
specific task for a specific dataset. In this work, we approach unification
from a complementary vantage point: unification across tasks and domains. To
this end, we explore the impact of discrete, learnt, time series data
representations that enable generalist, cross-domain training. Our method,
TOTEM, or TOkenized Time Series EMbeddings, proposes a simple tokenizer
architecture that embeds time series data from varying domains using a discrete
vectorized representation learned in a self-supervised manner. TOTEM works
across multiple tasks and domains with minimal to no tuning. We study the
efficacy of TOTEM with an extensive evaluation on 17 real world time series
datasets across 3 tasks. We evaluate both the specialist (i.e., training a
model on each domain) and generalist (i.e., training a single model on many
domains) settings, and show that TOTEM matches or outperforms previous best
methods on several popular benchmarks. The code can be found at:
this https URL

本研究探索了跨任务和领域的统一建模，通过使用离散的、学到的时间序列数据表示方法来实现通用、跨领域训练。我们提出了一种名为 TOTEM 的简单的分词架构，它使用自监督学习的方式来嵌入来自不同领域的时间序列数据。通过在 17 个真实世界的时间序列数据集上进行广泛评估，我们研究了 TOTEM 方法的有效性，涵盖了 3 个任务。我们对专家模式（在每个领域上训练模型）和通用模式（在多个领域上训练单个模型）进行了评估，并展示 TOTEM 在几个常见基准测试中能够达到或超过先前最佳方法的效果。

TOTEM: 基于令牌化的时间序列嵌入用于一般时间序列分析

TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis

We present a semi-supervised algorithm for lung cancer screening in which a
3D Convolutional Neural Network (CNN) is trained using the
Expectation-Maximization (EM) meta-algorithm. Semi-supervised learning allows a
smaller labelled data-set to be combined with an unlabeled data-set in order to
provide a larger and more diverse training sample. EM allows the algorithm to
simultaneously calculate a maximum likelihood estimate of the CNN training
coefficients along with the labels for the unlabeled training set which are
defined as a latent variable space. We evaluate the model performance of the
Semi-Supervised EM algorithm for CNNs through cross-domain training of the
Kaggle Data Science Bowl 2017 (Kaggle17) data-set with the National Lung
Screening Trial (NLST) data-set. Our results show that the Semi-Supervised EM
algorithm greatly improves the classification accuracy of the cross-domain lung
cancer screening, although results are lower than a fully supervised approach
with the advantage of additional labelled data from the unsupervised sample. As
such, we demonstrate that Semi-Supervised EM is a valuable technique to improve
the accuracy of lung cancer screening models using 3D CNNs.

提出一种基于半监督学习和 3D 卷积神经网络的肺癌筛查算法，采用 Expectation-Maximization 元算法进行训练，通过交叉域的方法验证其模型性能，结果表明该算法能够极大地提高肺癌筛查的分类准确性，证明了半监督 EM 是改进 3D CNN 肺癌筛查模型准确性的一种有效技术。

半监督肺癌筛查的深度期望最大化

Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening

Unsupervised domain adaptation aims to learn a model of classifier for
unlabeled samples on the target domain, given training data of labeled samples
on the source domain. Impressive progress is made recently by learning
invariant features via domain-adversarial training of deep networks. In spite
of the recent progress, domain adaptation is still limited in achieving the
invariance of feature distributions at a finer category level. To this end, we
propose in this paper a new domain adaptation method called Domain-Symmetric
Networks (SymNets). The proposed SymNet is based on a symmetric design of
source and target task classifiers, based on which we also construct an
additional classifier that shares with them its layer neurons. To train the
SymNet, we propose a novel adversarial learning objective whose key design is
based on a two-level domain confusion scheme, where the category-level
confusion loss improves over the domain-level one by driving the learning of
intermediate network features to be invariant at the corresponding categories
of the two domains. Both domain discrimination and domain confusion are
implemented based on the constructed additional classifier. Since target
samples are unlabeled, we also propose a scheme of cross-domain training to
help learn the target classifier. Careful ablation studies show the efficacy of
our proposed method. In particular, based on commonly used base networks, our
SymNets achieve the new state of the art on three benchmark domain adaptation
datasets.

本文提出一种新的无监督域适应方法 ——SymNets，它是基于一种对称的分类器设计的。我们提出了一种新的对抗学习目标，其中关键设计基于两级域混淆方案。我们还提出了一种跨域训练的方案来帮助学习目标分类器。针对常用的基础网络，我们的 SymNets 在三个基准域适应数据集上取得了新的最优性能。