Distance metric learning (DML) has been studied extensively in the past
decades for its superior performance with distance-based algorithms. Most of
the existing methods propose to learn a distance metric with pairwise or
triplet constraints. However, the number of constraints is quadratic or even
cubic in the number of the original examples, which makes it challenging for
DML to handle the large-scale data set. Besides, the real-world data may
contain various uncertainty, especially for the image data. The uncertainty can
mislead the learning procedure and cause the performance degradation. By
investigating the image data, we find that the original data can be observed
from a small set of clean latent examples with different distortions. In this
work, we propose the margin preserving metric learning framework to learn the
distance metric and latent examples simultaneously. By leveraging the ideal
properties of latent examples, the training efficiency can be improved
significantly while the learned metric also becomes robust to the uncertainty
in the original data. Furthermore, we can show that the metric is learned from
latent examples only, but it can preserve the large margin property even for
the original data. The empirical study on the benchmark image data sets
demonstrates the efficacy and efficiency of the proposed method.

本文提出了一种保持边界的度量学习框架，同时学习距离度量和潜在样本。该方法在处理大规模数据集时具有高效性，并且可以使学习的度量对数据不确定性具有鲁棒性，并且通过实验证明了该方法的有效性和高效性。

大规模距离度量学习及其不确定性

Large-scale Distance Metric Learning with Uncertainty

Analysing translation quality in regards to specific linguistic phenomena has
historically been difficult and time-consuming. Neural machine translation has
the attractive property that it can produce scores for arbitrary translations,
and we propose a novel method to assess how well NMT systems model specific
linguistic phenomena such as agreement over long distances, the production of
novel words, and the faithful translation of polarity. The core idea is that we
measure whether a reference translation is more probable under a NMT model than
a contrastive translation which introduces a specific type of error. We present
LingEval97, a large-scale data set of 97000 contrastive translation pairs based
on the WMT English->German translation task, with errors automatically created
with simple rules. We report results for a number of systems, and find that
recently introduced character-level NMT systems perform better at
transliteration than models with byte-pair encoding (BPE) segmentation, but
perform more poorly at morphosyntactic agreement, and translating discontiguous
units of meaning.

通过对神经机器翻译实现对特定语言现象的质量评估，我们提出了一种新方法并给出了包含 97000 对用于 WMT 英语 -> 德语翻译任务的对照翻译数据集 LingEval97，对字符级别和字节对编码（BPE）分割模型的实验结果显示，前者在翻译转写方面表现更好，但在形态句法协议和翻译非连续的意义单元方面表现较差。

字符级神经机器翻译的语法性如何？通过对比翻译对评估机器翻译质量

How Grammatical is Character-level Neural Machine Translation? Assessing  MT Quality with Contrastive Translation Pairs

We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research.

本文提出了四个数据驱动模型用于大规模的视觉推荐系统，这些模型可以有效地利用大量的时尚图片及其丰富的元数据，通过对大规模数据集的深入实验对这些算法进行了分析，并在颜色科学的基础上对其进行了基线，同时展示了从这些实验中学到的关键时尚见解并介绍了一个可以用于未来视觉研究的大规模时尚图像注释数据集（Fashion-136K）。