论文标题
用于监督学习的组不变张量火车网络
Group-invariant tensor train networks for supervised learning
论文作者
论文摘要
最近已证明不变性是机器学习模型中强大的归纳偏见。这样的一类预测模型是张量网络。我们引入了一种新的数值算法来构建在任意离散组的正常矩阵表示的作用下不变的张量的基础。此方法的数量级可以比以前的方法快几个数量级。然后将组不变张量合并为一个组不变张量训练网络，该网络可用作监督的机器学习模型。考虑到特定于问题的不知道，我们将该模型应用于蛋白质结合分类问题，并根据最新的深度学习方法获得了预测准确性。
Invariance has recently proven to be a powerful inductive bias in machine learning models. One such class of predictive or generative models are tensor networks. We introduce a new numerical algorithm to construct a basis of tensors that are invariant under the action of normal matrix representations of an arbitrary discrete group. This method can be up to several orders of magnitude faster than previous approaches. The group-invariant tensors are then combined into a group-invariant tensor train network, which can be used as a supervised machine learning model. We applied this model to a protein binding classification problem, taking into account problem-specific invariances, and obtained prediction accuracy in line with state-of-the-art deep learning approaches.
