Research on time-series similarity measures has emphasized the need for
elastic methods which align the indices of pairs of time series and a plethora
of non-parametric have been proposed for the task. On the other hand, deep
learning approaches are dominant in closely related domains, such as learning
image and text sentence similarity. In this paper, we propose
\textit{NeuralWarp}, a novel measure that models the alignment of time-series
indices in a deep representation space, by modeling a warping function as an
upper level neural network between deeply-encoded time series values.
Experimental results demonstrate that \textit{NeuralWarp} outperforms both
non-parametric and un-warped deep models on a range of diverse real-life
datasets.

该研究旨在提出一种新型的测量时间序列相似性的方法 ——NeuralWarp，通过建模深度表示空间中的时间序列索引的对齐模型，模拟乘法函数作为深层神经网络来建模。实验结果表明，相比于非参数方法和未调整的深层模型，NeuralWarp 在各种现实生活数据集上表现更好。

神经扭曲网络：时间序列相似度

NeuralWarp: Time-Series Similarity with Warping Networks

Many machine learning problems require the prediction of multi-dimensional
labels. Such structured prediction models can benefit from modeling
dependencies between labels. Recently, several deep learning approaches to
structured prediction have been proposed. Here we focus on capturing
cardinality constraints in such models. Namely, constraining the number of
non-zero labels that the model outputs. Such constraints have proven very
useful in previous structured prediction approaches, but it is a challenge to
introduce them into a deep learning framework. Here we show how to do this via
a novel deep architecture. Our approach outperforms strong baselines, achieving
state-of-the-art results on multi-label classification benchmarks.

本文介绍了一种基于深度学习的结构化预测模型，着重于在该类模型中捕捉参数约束。研究通过新颖的深度架构介绍了如何引入参数约束，并在多标签分类基准测试中取得了最先进的结果。

预测与约束：深度结构化预测中的基数建模

Predict and Constrain: Modeling Cardinality in Deep Structured  Prediction

There is an emerging trend to leverage noisy image datasets in many visual
recognition tasks. However, the label noise among the datasets severely
degenerates the \mbox{performance of deep} learning approaches. Recently, one
mainstream is to introduce the latent label to handle label noise, which has
shown promising improvement in the network designs. Nevertheless, the mismatch
between latent labels and noisy labels still affects the predictions in such
methods. To address this issue, we propose a quality embedding model, which
explicitly introduces a quality variable to represent the trustworthiness of
noisy labels. Our key idea is to identify the mismatch between the latent and
noisy labels by embedding the quality variables into different subspaces, which
effectively minimizes the noise effect. At the same time, the high-quality
labels is still able to be applied for training. To instantiate the model, we
further propose a Contrastive-Additive Noise network (CAN), which consists of
two important layers: (1) the contrastive layer estimates the quality variable
in the embedding space to reduce noise effect; and (2) the additive layer
aggregates the prior predictions and noisy labels as the posterior to train the
classifier. Moreover, to tackle the optimization difficulty, we deduce an SGD
algorithm with the reparameterization tricks, which makes our method scalable
to big data. We conduct the experimental evaluation of the proposed method over
a range of noisy image datasets. Comprehensive results have demonstrated CAN
outperforms the state-of-the-art deep learning approaches.

本文提出了一种质量嵌入模型，通过将质量变量嵌入到不同的子空间中，有效减小了噪声对标签的影响，并提出了一个对抗加性噪声网络模型（CAN）用于训练分类器，现有实验数据显示 CAN 在多种噪声图像数据集上优于深度学习等现有方法。