Machine learning-based techniques open up many opportunities and improvements
to derive deeper and more practical insights from data that can help businesses
make informed decisions. However, the majority of these techniques focus on the
conventional closed-set scenario, in which the label spaces for the training
and test sets are identical. Open set recognition (OSR) aims to bring
classification tasks in a situation that is more like reality, which focuses on
classifying the known classes as well as handling unknown classes effectively.
In such an open-set problem the gathered samples in the training set cannot
encompass all the classes and the system needs to identify unknown samples at
test time. On the other hand, building an accurate and comprehensive model in a
real dynamic environment presents a number of obstacles, because it is
prohibitively expensive to train for every possible example of unknown items,
and the model may fail when tested in testbeds. This study provides an
algorithm exploring a new representation of feature space to improve
classification in OSR tasks. The efficacy and efficiency of business processes
and decision-making can be improved by integrating OSR, which offers more
precise and insightful predictions of outcomes. We demonstrate the performance
of the proposed method on three established datasets. The results indicate that
the proposed model outperforms the baseline methods in accuracy and F1-score.

机器学习技术为数据提供更深入和实用的洞察力，帮助企业做出明智的决策；此研究提供了一种算法来改善开放集识别任务中的分类，通过探索特征空间的新表示方法，提高了分类的准确性。

通过开放集识别和未知样本检测的进展实现知情决策

Informed Decision-Making through Advancements in Open Set Recognition  and Unknown Sample Detection

End-to-end autonomous driving is a fully differentiable machine learning
system that takes raw sensor input data and other metadata as prior information
and directly outputs the ego vehicle's control signals or planned trajectories.
This paper attempts to systematically review all recent Machine Learning-based
techniques to perform this end-to-end task, including, but not limited to,
object detection, semantic scene understanding, object tracking, trajectory
predictions, trajectory planning, vehicle control, social behavior, and
communications. This paper focuses on recent fully differentiable end-to-end
reinforcement learning and deep learning-based techniques. Our paper also
builds taxonomies of the significant approaches by sub-grouping them and
showcasing their research trends. Finally, this survey highlights the open
challenges and points out possible future directions to enlighten further
research on the topic.

综述了近期基于机器学习的技术，包括但不限于目标检测、轨迹预测与强化学习，以及对其进行分类和研究趋势，同时指出了该领域的挑战和未来研究方向。