Data augmentation is an essential building block for learning efficient deep
learning models. Among all augmentation techniques proposed so far, linear
interpolation of training data points, also called mixup, has found to be
effective for a large panel of applications. While the majority of works have
focused on selecting the right points to mix, or applying complex non-linear
interpolation, we are interested in mixing similar points more frequently and
strongly than less similar ones. To this end, we propose to dynamically change
the underlying distribution of interpolation coefficients through warping
functions, depending on the similarity between data points to combine. We
define an efficient and flexible framework to do so without losing in
diversity. We provide extensive experiments for classification and regression
tasks, showing that our proposed method improves both performance and
calibration of models. Code available in
this https URL

动态地改变插值系数的分布以通过变形函数混合相似数据点，提出了一种既高效又灵活的框架，这样做既不会损失多样性，又会提高模型的性能和校准性。

使用核变形函数为数据定制混合策略

Tailoring Mixup to Data using Kernel Warping functions

A parametric point process model is developed, with modeling based on the
assumption that sequential observations often share latent phenomena, while
also possessing idiosyncratic effects. An alternating optimization method is
proposed to learn a "registered" point process that accounts for shared
structure, as well as "warping" functions that characterize idiosyncratic
aspects of each observed sequence. Under reasonable constraints, in each
iteration we update the sample-specific warping functions by solving a set of
constrained nonlinear programming problems in parallel, and update the model by
maximum likelihood estimation. The justifiability, complexity and robustness of
the proposed method are investigated in detail, and the influence of sequence
stitching on the learning results is examined empirically. Experiments on both
synthetic and real-world data demonstrate that the method yields explainable
point process models, achieving encouraging results compared to
state-of-the-art methods.

提出一种基于参数的点过程模型，并使用交替优化方法学习‘注册’点过程和描述每个观测序列特异性方面的‘弯曲’函数。在合理的限制下，通过同时求解一组约束非线性规划问题更新样本特定的弯曲函数，并通过最大似然估计更新模型，实验所得结果表明，该方法比现有方法更为可解释，实现了令人鼓舞的结果。