Type inference over partial contexts in dynamically typed languages is
challenging. In this work, we present a graph neural network model that
predicts types by probabilistically reasoning over a program's structure,
names, and patterns. The network uses deep similarity learning to learn a
TypeSpace -- a continuous relaxation of the discrete space of types -- and how
to embed the type properties of a symbol (i.e. identifier) into it.
Importantly, our model can employ one-shot learning to predict an open
vocabulary of types, including rare and user-defined ones. We realise our
approach in Typilus for Python that combines the TypeSpace with an optional
type checker. We show that Typilus accurately predicts types. Typilus
confidently predicts types for 70% of all annotatable symbols; when it predicts
a type, that type optionally type checks 95% of the time. Typilus can also find
incorrect type annotations; two important and popular open source libraries,
fairseq and allennlp, accepted our pull requests that fixed the annotation
errors Typilus discovered.

本研究提出一种基于图神经网络的类型推断模型 Typilus，借助深刻相似度学习从程序结构、名称和模式角度预测变量类型，模型可采用一次学习预测任何罕见和用户定义的类型，成功应用于 Python，并能够精准地预测符号类型并找到不正确的类型注释。

Typilus: 神经类型提示

Typilus: Neural Type Hints

In recent years, person re-identification (re-id) catches great attention in
both computer vision community and industry. In this paper, we propose a new
framework for person re-identification with a triplet-based deep similarity
learning using convolutional neural networks (CNNs). The network is trained
with triplet input: two of them have the same class labels and the other one is
different. It aims to learn the deep feature representation, with which the
distance within the same class is decreased, while the distance between the
different classes is increased as much as possible. Moreover, we trained the
model jointly on six different datasets, which differs from common practice -
one model is just trained on one dataset and tested also on the same one.
However, the enormous number of possible triplet data among the large number of
training samples makes the training impossible. To address this challenge, a
double-sampling scheme is proposed to generate triplets of images as effective
as possible. The proposed framework is evaluated on several benchmark datasets.
The experimental results show that, our method is effective for the task of
person re-identification and it is comparable or even outperforms the
state-of-the-art methods.

本文提出了基于三元组卷积神经网络的深度相似性学习框架，采用多数据集训练和双重采样方案，实现了在行人再识别任务中的有效性并表现出与甚至优于现有最先进方法的性能。