  Transformer-based neural network architectures achieve state-of-the-art
results in different domains, from natural language processing (NLP) to
computer vision (CV). The key idea of Transformers, the attention mechanism,
has already led to significant breakthroughs in many areas. The attention has
found their implementation for time series data as well. However, due to the
quadratic complexity of the attention calculation regarding input sequence
length, the application of Transformers is limited by high resource demands.
Moreover, their modifications for industrial time series need to be robust to
missing or noised values, which complicates the expansion of the horizon of
their application. To cope with these issues, we introduce the class of
efficient Transformers named Regularized Transformers (Reguformers). We
implement the regularization technique inspired by the dropout ideas to improve
robustness and reduce computational expenses. The focus in our experiments is
on oil&gas data, namely, well logs, a prominent example of multivariate time
series. The goal is to solve the problems of similarity and representation
learning for them. To evaluate our models for such problems, we work with an
industry-scale open dataset consisting of well logs of more than 20 wells. The
experiments show that all variations of Reguformers outperform the previously
developed RNNs, classical Transformer model, and robust modifications of it
like Informer and Performer in terms of well-intervals' classification and the
quality of the obtained well-intervals' representations. Moreover, the
sustainability to missing and incorrect data in our models exceeds that of
others by a significant margin. The best result that the Reguformer achieves on
well-interval similarity task is the mean PR~AUC score equal to 0.983, which is
comparable to the classical Transformer and outperforms the previous models.
