Continuous machine learning pipelines are common in industrial settings where
models are periodically trained on data streams. Unfortunately, concept drifts
may occur in data streams where the joint distribution of the data X and label
y, P(X, y), changes over time and possibly degrade model accuracy. Existing
concept drift adaptation approaches mostly focus on updating the model to the
new data possibly using ensemble techniques of previous models and tend to
discard the drifted historical data. However, we contend that explicitly
utilizing the drifted data together leads to much better model accuracy and
propose Quilt, a data-centric framework for identifying and selecting data
segments that maximize model accuracy. To address the potential downside of
efficiency, Quilt extends existing data subset selection techniques, which can
be used to reduce the training data without compromising model accuracy. These
techniques cannot be used as is because they only assume virtual drifts where
the posterior probabilities P(y|X) are assumed not to change. In contrast, a
key challenge in our setup is to also discard undesirable data segments with
concept drifts. Quilt thus discards drifted data segments and selects data
segment subsets holistically for accurate and efficient model training. The two
operations use gradient-based scores, which have little computation overhead.
In our experiments, we show that Quilt outperforms state-of-the-art drift
adaptation and data selection baselines on synthetic and real datasets.

通过使用 Quilt，这是一个数据中心的框架来鉴定和选择能够最大化模型准确性的数据片段，从而提高模型准确性和效率。

Quilt：面对概念漂移的鲁棒数据段选择

Quilt: Robust Data Segment Selection against Concept Drifts

Quantum computers can theoretically have significant acceleration over
classical computers; but, the near-future era of quantum computing is limited
due to small number of qubits that are also error prone. Quilt is a framework
for performing multi-class classification task designed to work effectively on
current error-prone quantum computers. Quilt is evaluated with real quantum
machines as well as with projected noise levels as quantum machines become more
noise-free. Quilt demonstrates up to 85% multi-class classification accuracy
with the MNIST dataset on a five-qubit system.

量子计算机可以在理论上显著加速经典计算机；但是，由于量子比特数量有限且容易出错，近期的量子计算机时代受到限制。 Quilt 是一个针对当前容易出错的量子计算机设计的执行多类分类任务的框架。使用真实的量子机器和预期的噪音水平对 Quilt 进行评估，随着量子机器变得更加噪音自由。在一个五比特系统上，Quilt 展示了 MNIST 数据集多类分类准确度高达 85％。

QUILT: 在量子计算机上利用多种不同的量子分类器集成进行有效的多类别分类

QUILT: Effective Multi-Class Classification on Quantum Computers Using  an Ensemble of Diverse Quantum Classifiers

Recent accelerations in multi-modal applications have been made possible with
the plethora of image and text data available online. However, the scarcity of
analogous data in the medical field, specifically in histopathology, has halted
comparable progress. To enable similar representation learning for
histopathology, we turn to YouTube, an untapped resource of videos, offering
$1,087$ hours of valuable educational histopathology videos from expert
clinicians. From YouTube, we curate Quilt: a large-scale vision-language
dataset consisting of $768,826$ image and text pairs. Quilt was automatically
curated using a mixture of models, including large language models, handcrafted
algorithms, human knowledge databases, and automatic speech recognition. In
comparison, the most comprehensive datasets curated for histopathology amass
only around $200$K samples. We combine Quilt with datasets from other sources,
including Twitter, research papers, and the internet in general, to create an
even larger dataset: Quilt-1M, with $1$M paired image-text samples, marking it
as the largest vision-language histopathology dataset to date. We demonstrate
the value of Quilt-1M by fine-tuning a pre-trained CLIP model. Our model
outperforms state-of-the-art models on both zero-shot and linear probing tasks
for classifying new histopathology images across $13$ diverse patch-level
datasets of $8$ different sub-pathologies and cross-modal retrieval tasks.

通过从 YouTube 等多种资源中收集图像和文本，我们构建了一个大规模的视觉语言数据集 Quilt-1M，其中共包含 100 万个成对的图像和文本样本，这是到目前为止规模最大的组织学图像和文本数据集，并通过微调预训练的 CLIP 模型在 13 个 diverse patch-level 数据集和跨模态检索任务中优于最先进的模型。