论文标题
从序列中无监督对模棱两可的结构的学习
Unsupervised Learning of Equivariant Structure from Sequences
论文作者
论文摘要
在这项研究中，我们提出了元时间序列预测（MSP），这是一个无监督的框架，可以从至少三个长度的时间顺序中学习对称性。我们的方法通过简单地简单地训练Encoder-Decoder模型来预测未来的观察结果，从而利用时间顺序的固定属性（例如，时间顺序的恒定速度，恒定加速度）来学习数据集的基本模棱两可的结构。我们将证明，借助我们的框架，数据集的隐藏解开结构自然地作为副产品出现，通过将同时块 - 二分化化应用于潜在空间中的过渡算子，该过程在表示理论中通常使用，该过程基于对小组动作的响应类型来分解功能空间。我们将从经验和理论角度展示我们的方法。我们的结果表明，找到一个简单的结构化关系并学习具有外推能力的模型是同一硬币的两个方面。该代码可在https://github.com/takerum/meta_secerential_prediction上找到。
In this study, we present meta-sequential prediction (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. Our method leverages the stationary property (e.g. constant velocity, constant acceleration) of the time sequence to learn the underlying equivariant structure of the dataset by simply training the encoder-decoder model to be able to predict the future observations. We will demonstrate that, with our framework, the hidden disentangled structure of the dataset naturally emerges as a by-product by applying simultaneous block-diagonalization to the transition operators in the latent space, the procedure which is commonly used in representation theory to decompose the feature-space based on the type of response to group actions. We will showcase our method from both empirical and theoretical perspectives. Our result suggests that finding a simple structured relation and learning a model with extrapolation capability are two sides of the same coin. The code is available at https://github.com/takerum/meta_sequential_prediction.
