Imbalanced classification problems are extremely common in natural language
processing and are solved using a variety of resampling and filtering
techniques, which often involve making decisions on how to select training data
or decide which test examples should be labeled by the model. We examine the
tradeoffs in model performance involved in choices of training sample and
filter training and test data in heavily imbalanced token classification task
and examine the relationship between the magnitude of these tradeoffs and the
base rate of the phenomenon of interest. In experiments on sequence tagging to
detect rare phenomena in English and Arabic texts, we find that different
methods of selecting training data bring tradeoffs in effectiveness and
efficiency. We also see that in highly imbalanced cases, filtering test data
using first-pass retrieval models is as important for model performance as
selecting training data. The base rate of a rare positive class has a clear
effect on the magnitude of the changes in performance caused by the selection
of training or test data. As the base rate increases, the differences brought
about by those choices decreases.

在处理高度不平衡的标记分类任务中，选择训练数据的方式会对模型性能产生效果和效率方面上的权衡。在使用英语和阿拉伯语文本序列标记实验中，不同的训练数据选择方法带来了不同的效果和效率权衡；在对高度不平衡情况下的模型性能测试中，使用基于一次检索模型的测试数据过滤和训练数据选择同样重要。而变化的幅度取决于所研究的现象基础率。

不均衡分类中的重采样和滤波权衡

Tradeoffs in Resampling and Filtering for Imbalanced Classification

Edges are image locations where the gray value intensity changes suddenly.
They are among the most important features to understand and segment an image.
Edge detection is a standard task in digital image processing, solved for
example using filtering techniques. However, the amount of data to be processed
grows rapidly and pushes even supercomputers to their limits. Quantum computing
promises exponentially lower memory usage in terms of the number of qubits
compared to the number of classical bits. In this paper, we propose a hybrid
method for quantum edge detection based on the idea of a quantum artificial
neuron. Our method can be practically implemented on quantum computers,
especially on those of the current noisy intermediate-scale quantum era. We
compare six variants of the method to reduce the number of circuits and thus
the time required for the quantum edge detection. Taking advantage of the
scalability of our method, we can practically detect edges in images
considerably larger than reached before.

本文提出基于量子人工神经元思想的混合量子边缘检测方法，可在现阶段的噪声中等规模的量子计算机上实现，并通过方法的可扩展性实现了比以往更大范围的图像边缘检测。

一种混合量子图像边缘检测器在 NISQ 时代的应用

A hybrid quantum image edge detector for the NISQ era

One challenge with neural ranking is the need for a large amount of
manually-labeled relevance judgments for training. In contrast with prior work,
we examine the use of weak supervision sources for training that yield pseudo
query-document pairs that already exhibit relevance (e.g., newswire
headline-content pairs and encyclopedic heading-paragraph pairs). We also
propose filtering techniques to eliminate training samples that are too far out
of domain using two techniques: a heuristic-based approach and novel supervised
filter that re-purposes a neural ranker. Using several leading neural ranking
architectures and multiple weak supervision datasets, we show that these
sources of training pairs are effective on their own (outperforming prior weak
supervision techniques), and that filtering can further improve performance.

论文研究了神经排序中标记相关性判定数量巨大的问题，并提出使用弱监督来源训练排名器，然后使用过滤技术排除域外样本，实现了有效的性能改进。