Shapelets are discriminative subsequences, originally embedded in
shapelet-based decision trees but have since been extended to shapelet-based
transformations. We propose Castor, a simple, efficient, and accurate time
series classification algorithm that utilizes shapelets to transform time
series. The transformation organizes shapelets into groups with varying
dilation and allows the shapelets to compete over the time context to construct
a diverse feature representation. By organizing the shapelets into groups, we
enable the transformation to transition between levels of competition,
resulting in methods that more closely resemble distance-based transformations
or dictionary-based transformations. We demonstrate, through an extensive
empirical investigation, that Castor yields transformations that result in
classifiers that are significantly more accurate than several state-of-the-art
classifiers. In an extensive ablation study, we examine the effect of choosing
hyperparameters and suggest accurate and efficient default values.

通过组织 shapelets 并使其在不同时间上竞争来构建多样的特征表示，Castor 算法利用 shapelets 对时间序列进行转换，通过广泛的实证研究证明 Castor 提供了比多个最先进的分类器更准确的转换，并且在广泛的消融研究中，我们检验了选择超参数的影响并提供了准确且高效的默认值。

Castor：用于快速准确的时间序列分类的竞争形状片段

Castor: Competing shapelets for fast and accurate time series  classification

The Long-Tailed Recognition (LTR) problem emerges in the context of learning
from highly imbalanced datasets, in which the number of samples among different
classes is heavily skewed. LTR methods aim to accurately learn a dataset
comprising both a larger Head set and a smaller Tail set. We propose a theorem
where under the assumption of strong convexity of the loss function, the
weights of a learner trained on the full dataset are within an upper bound of
the weights of the same learner trained strictly on the Head. Next, we assert
that by treating the learning of the Head and Tail as two separate and
sequential steps, Continual Learning (CL) methods can effectively update the
weights of the learner to learn the Tail without forgetting the Head. First, we
validate our theoretical findings with various experiments on the toy MNIST-LT
dataset. We then evaluate the efficacy of several CL strategies on multiple
imbalanced variations of two standard LTR benchmarks (CIFAR100-LT and
CIFAR10-LT), and show that standard CL methods achieve strong performance gains
in comparison to baselines and approach solutions that have been tailor-made
for LTR. We also assess the applicability of CL techniques on real-world data
by exploring CL on the naturally imbalanced Caltech256 dataset and demonstrate
its superiority over state-of-the-art classifiers. Our work not only unifies
LTR and CL but also paves the way for leveraging advances in CL methods to
tackle the LTR challenge more effectively.

本文提出了解决长尾识别问题的两个重要方法：理论公式证明将全数据集训练的权重控制在同样训练头部数据的权重上限范围内；将头部和尾部的学习视为分开的顺序步骤，使用 Continual Learning 方法来有效地更新学习者的权重。作者在 CIFAR100-LT，CIFAR10-LT 和 Caltech-256 等数据集上执行的实验结果表明，Continual Learning 方法可以比 LTR 专用解决方案更有效地解决长尾识别问题。

持续学习是否能提高长尾识别？朝向一个统一框架

Can Continual Learning Improve Long-Tailed Recognition? Toward a Unified  Framework

Hate speech detection has become a hot topic in recent years due to the
exponential growth of offensive language in social media. It has proven that,
state-of-the-art hate speech classifiers are efficient only when tested on the
data with the same feature distribution as training data. As a consequence,
model architecture plays the second role to improve the current results. In
such a diverse data distribution relying on low level features is the main
cause of deficiency due to natural bias in data. That's why we need to use high
level features to avoid a biased judgement. In this paper, we statistically
analyze the Perspective Scores and their impact on hate speech detection. We
show that, different hate speech datasets are very similar when it comes to
extract their Perspective Scores. Eventually, we prove that, over-sampling the
Perspective Scores of a hate speech dataset can significantly improve the
generalization performance when it comes to be tested on other hate speech
datasets.

本文研究了如何使用高级特征来避免数据偏差，并统计分析了不同仇恨言论数据集的角度得分，最终证明了在其他仇恨言论数据集上测试时，过采样角度得分可以显著提高泛化性能。