Machine learning from data streams is an active and growing research area.
Research on learning from streaming data typically makes strict assumptions
linked to computational resource constraints, including requirements for stream
mining algorithms to inspect each instance not more than once and be ready to
give a prediction at any time. Here we review the historical context of data
streams research placing the common assumptions used in machine learning over
data streams in their historical context.

数据流机器学习是一个活跃且不断发展的研究领域，本文回顾了数据流研究的历史背景，并将其与机器学习在数据流中的常见假设联系起来。

数据流的历史背景

A Historical Context for Data Streams

Neural Architecture Search (NAS) refers to automatically design the
architecture. We propose an hourglass-inspired approach (HourNAS) for this
problem that is motivated by the fact that the effects of the architecture
often proceed from the vital few blocks. Acting like the narrow neck of an
hourglass, vital blocks in the guaranteed path from the input to the output of
a deep neural network restrict the information flow and influence the network
accuracy. The other blocks occupy the major volume of the network and determine
the overall network complexity, corresponding to the bulbs of an hourglass. To
achieve an extremely fast NAS while preserving the high accuracy, we propose to
identify the vital blocks and make them the priority in the architecture
search. The search space of those non-vital blocks is further shrunk to only
cover the candidates that are affordable under the computational resource
constraints. Experimental results on the ImageNet show that only using 3 hours
(0.1 days) with one GPU, our HourNAS can search an architecture that achieves a
77.0% Top-1 accuracy, which outperforms the state-of-the-art methods.

我们提出了一种基于沙漏启发式方法的神经架构搜索算法，该算法能通过识别关键块并将其优先考虑，从而快速搜索出网络架构，同时保持准确性，并且在计算资源的限制下，进一步缩小了搜索空间，获得了 77.0％的 Top-1 准确率，超过了最先进的方法。