排序是计算机内经常进行的一种操作,其目的是将一组“无序”的记录序列调整为“有序”的记录序列。分内部排序和外部排序。若整个排序过程不需要访问外存便能完成,则称此类排序问题为内部排序。反之,若参加排序的记录数量很大,整个序列的排序过程不可能在内存中完成,则称此类排序问题为外部排序。内部排序的过程是一个逐步扩大记录的有序序列长度的过程。

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https://arxiv.org/abs/2002.12312

在这篇论文中,我们讨论了协同过滤和排名的一些最新进展。第一章简要介绍了协同过滤与排名的历史与现状;第二章首先讨论了图信息的点态协同过滤问题,以及我们提出的新方法如何对深度图信息进行编码,这有助于现有的四种图信息协同过滤算法;第三章介绍了协同排序的配对方法,以及如何将算法加速到接近线性的时间复杂度;第4章是关于新的列表方法的协作排名,以及如何更好的选择列表方法的损失显式和隐式反馈超过点和两两损失;第5章是关于我们提出的新的正则化技术——随机共享嵌入(SSE),以及它在6个不同的任务(包括推荐和自然语言处理)中的理论有效性和经验有效性;第6章是我们如何在SSE的帮助下,为最先进的序列推荐模型引入个性化,这对于防止我们的个性化模型对训练数据的过度拟合起到了重要的作用;第7章,我们总结了目前所取得的成果,并展望了未来的发展方向;第八章是所有章节的附录。

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Predicting the motion of agents such as pedestrians or human-driven vehicles is one of the most critical problems in the autonomous driving domain. The overall safety of driving and the comfort of a passenger directly depend on its successful solution. The motion prediction problem also remains one of the most challenging problems in autonomous driving engineering, mainly due to high variance of the possible agent's future behavior given a situation. The two phenomena responsible for the said variance are the multimodality caused by the uncertainty of the agent's intent (e.g., turn right or move forward) and uncertainty in the realization of a given intent (e.g., which lane to turn into). To be useful within a real-time autonomous driving pipeline, a motion prediction system must provide efficient ways to describe and quantify this uncertainty, such as computing posterior modes and their probabilities or estimating density at the point corresponding to a given trajectory. It also should not put substantial density on physically impossible trajectories, as they can confuse the system processing the predictions. In this paper, we introduce the PRANK method, which satisfies these requirements. PRANK takes rasterized bird-eye images of agent's surroundings as an input and extracts features of the scene with a convolutional neural network. It then produces the conditional distribution of agent's trajectories plausible in the given scene. The key contribution of PRANK is a way to represent that distribution using nearest-neighbor methods in latent trajectory space, which allows for efficient inference in real time. We evaluate PRANK on the in-house and Argoverse datasets, where it shows competitive results.

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