Hardware-aware Neural Architecture Search (HW-NAS) is increasingly being used
to design efficient deep learning architectures. An efficient and flexible
search space is crucial to the success of HW-NAS. Current approaches focus on
designing a macro-architecture and searching for the architecture's
hyperparameters based on a set of possible values. This approach is biased by
the expertise of deep learning (DL) engineers and standard modeling approaches.
In this paper, we present a Grassroots Operator Search (GOS) methodology. Our
HW-NAS adapts a given model for edge devices by searching for efficient
operator replacement. We express each operator as a set of mathematical
instructions that capture its behavior. The mathematical instructions are then
used as the basis for searching and selecting efficient replacement operators
that maintain the accuracy of the original model while reducing computational
complexity. Our approach is grassroots since it relies on the mathematical
foundations to construct new and efficient operators for DL architectures. We
demonstrate on various DL models, that our method consistently outperforms the
original models on two edge devices, namely Redmi Note 7S and Raspberry Pi3,
with a minimum of 2.2x speedup while maintaining high accuracy. Additionally,
we showcase a use case of our GOS approach in pulse rate estimation on
wristband devices, where we achieve state-of-the-art performance, while
maintaining reduced computational complexity, demonstrating the effectiveness
of our approach in practical applications.

我们提出了一种基于数学基础构造新的高效运算符以降低计算复杂度并保持模型精度的 Grassroots 操作符搜索方法，并在各种深度学习模型和边缘设备上展示了其在提高性能和实用应用中的有效性。

基层运营商模型边缘适应搜索

Grassroots Operator Search for Model Edge Adaptation

Transfer learning boosts the performance of medical image analysis by
enabling deep learning (DL) on small datasets through the knowledge acquired
from large ones. As the number of DL architectures explodes, exhaustively
attempting all candidates becomes unfeasible, motivating cheaper alternatives
for choosing them. Transferability scoring methods emerge as an enticing
solution, allowing to efficiently calculate a score that correlates with the
architecture accuracy on any target dataset. However, since transferability
scores have not been evaluated on medical datasets, their use in this context
remains uncertain, preventing them from benefiting practitioners. We fill that
gap in this work, thoroughly evaluating seven transferability scores in three
medical applications, including out-of-distribution scenarios. Despite
promising results in general-purpose datasets, our results show that no
transferability score can reliably and consistently estimate target performance
in medical contexts, inviting further work in that direction.

通过对三个医学应用进行深入评估，我们的研究表明没有任何可靠和一致的迁移性评分能够准确估计医学环境下的目标性能，这为进一步研究指明了方向。

迁移性指标的表现在医学任务中不可靠

The Performance of Transferability Metrics does not Translate to Medical  Tasks

In this paper, we present a dataset for the computational study of a number
of Modern Greek dialects. It consists of raw text data from four dialects of
Modern Greek, Cretan, Pontic, Northern Greek and Cypriot Greek. The dataset is
of considerable size, albeit imbalanced, and presents the first attempt to
create large scale dialectal resources of this type for Modern Greek dialects.
We then use the dataset to perform dialect idefntification. We experiment with
traditional ML algorithms, as well as simple DL architectures. The results show
very good performance on the task, potentially revealing that the dialects in
question have distinct enough characteristics allowing even simple ML models to
perform well on the task. Error analysis is performed for the top performing
algorithms showing that in a number of cases the errors are due to insufficient
dataset cleaning.

此研究论文介绍了一个用于计算研究现代希腊方言的数据集，其中包括克里特岛、庇安提、北希腊和塞浦路斯希腊四种方言的原始文本数据。该数据集规模相当大，尽管存在不平衡问题，但是它是第一个针对现代希腊方言创造大规模方言资源的尝试。我们使用该数据集进行方言识别，并进行了传统机器学习算法和简单深度学习架构的实验。结果表明，这些方言在任务中具有足够独特的特征，即使是简单的机器学习模型也能表现出很好的性能。对表现最佳算法进行误差分析后发现，在一些情况下错误是由于数据集清理不足造成的。