Approximate k-Nearest Neighbour (ANN) methods are often used for mining
information and aiding machine learning on large scale high-dimensional
datasets. ANN methods typically differ in the index structure used for
accelerating searches, resulting in various recall/runtime trade-off points.
For applications with static datasets, runtime constraints and dataset
properties can be used to empirically select an ANN method with suitable
operating characteristics. However, for applications with dynamic datasets,
which are subject to frequent online changes (like addition of new samples),
there is currently no consensus as to which ANN methods are most suitable.
Traditional evaluation approaches do not consider the computational costs of
updating the index structure, as well as the frequency and size of index
updates. To address this, we empirically evaluate 5 popular ANN methods on two
main applications (online data collection and online feature learning) while
taking into account these considerations. Two dynamic datasets are used,
derived from the SIFT1M dataset with 1 million samples and the DEEP1B dataset
with 1 billion samples. The results indicate that the often used k-d trees
method is not suitable on dynamic datasets as it is slower than a
straightforward baseline exhaustive search method. For online data collection,
the Hierarchical Navigable Small World Graphs method achieves a consistent
speedup over baseline across a wide range of recall rates. For online feature
learning, the Scalable Nearest Neighbours method is faster than baseline for
recall rates below 75%.

近似 k 最近邻（ANN）方法常用于大规模高维数据集上的信息挖掘和机器学习，针对动态数据集和在线特征学习等应用，我们通过实证评估了 5 种流行的 ANN 方法，结果表明在动态数据集中，k-d 树方法不适用，并且在在线数据收集和在线特征学习方面，层次可导航小世界图方法和可扩展最近邻方法分别比基线方法更快速。

动态数据集上的近似最近邻搜索：一项调查

Approximate Nearest Neighbour Search on Dynamic Datasets: An  Investigation

Clustering in dynamic environments is of increasing importance, with broad
applications ranging from real-time data analysis and online unsupervised
learning to dynamic facility location problems. While meta-heuristics have
shown promising effectiveness in static clustering tasks, their application for
tracking optimal clustering solutions or robust clustering over time in dynamic
environments remains largely underexplored. This is partly due to a lack of
dynamic datasets with diverse, controllable, and realistic dynamic
characteristics, hindering systematic performance evaluations of clustering
algorithms in various dynamic scenarios. This deficiency leads to a gap in our
understanding and capability to effectively design algorithms for clustering in
dynamic environments. To bridge this gap, this paper introduces the Dynamic
Dataset Generator (DDG). DDG features multiple dynamic Gaussian components
integrated with a range of heterogeneous, local, and global changes. These
changes vary in spatial and temporal severity, patterns, and domain of
influence, providing a comprehensive tool for simulating a wide range of
dynamic scenarios.

本研究提出了动态数据集生成器（DDG），用于模拟各种动态场景，帮助解决在动态环境中的聚类问题。

动态环境中的聚类：异质变化数据集生成框架

Clustering in Dynamic Environments: A Framework for Benchmark Dataset  Generation With Heterogeneous Changes

While Large Language Models (LLMs) excel at the Winograd Schema Challenge
(WSC), a coreference resolution task testing common-sense reasoning through
pronoun disambiguation, they struggle with instances that feature minor
alterations or rewording. To address this, we introduce EvoGrad, an open-source
platform that harnesses a human-in-the-loop approach to create a dynamic
dataset tailored to such altered WSC instances. Leveraging ChatGPT's
capabilities, we expand our task instances from 182 to 3,691, setting a new
benchmark for diverse common-sense reasoning datasets. Additionally, we
introduce the error depth metric, assessing model stability in dynamic tasks.
Our results emphasize the challenge posed by EvoGrad: Even the best performing
LLM, GPT-3.5, achieves an accuracy of 65.0% with an average error depth of 7.2,
a stark contrast to human performance of 92. 8% accuracy without perturbation
errors. This highlights ongoing model limitations and the value of dynamic
datasets in uncovering them.

利用人工智能辅助技术，我们引入 EvoGrad 平台，拓展了 Winograd Schema Challenge 任务实例数量，从 182 个扩展到了 3,691 个，为多样化的常识推理数据集设定了新的基准。通过引入错误深度度量，我们评估了模型在动态任务中的稳定性。我们的研究结果凸显了 EvoGrad 提出的挑战：即使是表现最好的大型语言模型 GPT-3.5，在准确率上也只有 65.0％，平均错误深度为 7.2，与人类准确率 92.8％相比存在显著差距，这突出了模型的局限性和动态数据集的价值。

EvoGrad: 以人类对手为基础的 Winograd 模式挑战的动态方法

EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human  Adversaries

Currently, food image recognition tasks are evaluated against fixed datasets.
However, in real-world conditions, there are cases in which the number of
samples in each class continues to increase and samples from novel classes
appear. In particular, dynamic datasets in which each individual user creates
samples and continues the updating process often have content that varies
considerably between different users, and the number of samples per person is
very limited. A single classifier common to all users cannot handle such
dynamic data. Bridging the gap between the laboratory environment and the real
world has not yet been accomplished on a large scale. Personalizing a
classifier incrementally for each user is a promising way to do this. In this
paper, we address the personalization problem, which involves adapting to the
user's domain incrementally using a very limited number of samples. We propose
a simple yet effective personalization framework which is a combination of the
nearest class mean classifier and the 1-nearest neighbor classifier based on
deep features. To conduct realistic experiments, we made use of a new dataset
of daily food images collected by a food-logging application. Experimental
results show that our proposed method significantly outperforms existing
methods.

本文讨论了基于动态数据的食品图像识别问题，并提出了一种基于最近类均值分类器和深度特征的 1 - 最近邻分类器组合的个性化框架，此方法在实验中表现明显优于现有方法。