A central mechanism in machine learning is to identify, store, and recognize
patterns. How to learn, access, and retrieve such patterns is crucial in
Hopfield networks and the more recent transformer architectures. We show that
the attention mechanism of transformer architectures is actually the update
rule of modern Hopfield networks that can store exponentially many patterns. We
exploit this high storage capacity of modern Hopfield networks to solve a
challenging multiple instance learning (MIL) problem in computational biology:
immune repertoire classification. Accurate and interpretable machine learning
methods solving this problem could pave the way towards new vaccines and
therapies, which is currently a very relevant research topic intensified by the
COVID-19 crisis. Immune repertoire classification based on the vast number of
immunosequences of an individual is a MIL problem with an unprecedentedly
massive number of instances, two orders of magnitude larger than currently
considered problems, and with an extremely low witness rate. In this work, we
present our novel method DeepRC that integrates transformer-like attention, or
equivalently modern Hopfield networks, into deep learning architectures for
massive MIL such as immune repertoire classification. We demonstrate that
DeepRC outperforms all other methods with respect to predictive performance on
large-scale experiments, including simulated and real-world virus infection
data, and enables the extraction of sequence motifs that are connected to a
given disease class. Source code and datasets: this https URL

研究表明，转换器体系结构的关注机制实际上是现代 Hopfield 网络更新规则，可以存储指数级多个模式，在免疫库分类等具有大量示例的问题中可以利用这种高存储容量来解决问题。通过将转换器类似的关注与现代 Hopfield 网络相结合，我们提出了 DeepRC 方法，对于包括模拟和真实世界病毒感染数据在内的大规模实验，DeepRC 能够在预测性能方面胜过所有其他方法，并可以提取与给定疾病类别相连的序列模体。