Alexander Kovalenko

Alexander Kovalenko, Vitaliy Pozdnyakov, Ilya Makarov

Timely detected anomalies in the chemical technological processes, as well as the earliest detection of the cause of the fault, significantly reduce the production cost in the industrial factories. Data on the state of the technological process and the operation of production equipment are received by a large number of different sensors. To better predict the behavior of the process and equipment, it is necessary not only to consider the behavior of the signals in each sensor separately, but also to take into account their correlation and hidden relationships with each other. Graph-based data representation helps with this. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. In this work, the possibility of applying graph neural networks to the problem of fault diagnosis in a chemical process is studied. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance. In this work, several methods for obtaining adjacency matrices were considered, as well as their quality was studied. It has also been proposed to use multiple adjacency matrices in one model. We showed state-of-the-art performance on the fault diagnosis task with the Tennessee Eastman Process dataset. The proposed graph neural networks outperformed the results of recurrent neural networks.

Uladzislau Yorsh, Alexander Kovalenko, Vojtěch Vančura et al.

In this paper, we propose that the dot product pairwise matching attention layer, which is widely used in Transformer-based models, is redundant for the model performance. Attention, in its original formulation, has to be seen rather as a human-level tool to explore and/or visualize relevancy scores in sequential data. However, the way how it is constructed leads to significant computational complexity. Instead, we present SimpleTRON: Simple Transformer with O(N) Complexity, a simple and fast alternative without any approximation that, unlike other approximation models, does not have any architecture-related overhead and therefore can be seen as a purely linear Transformer-like model. This architecture, to the best of our knowledge, outperforms existing sub-quadratic attention approximation models on several tasks from the Long-Range Arena benchmark. Moreover, we show, that SimpleTRON can benefit from weight transfer from pretrained large language models, as its parameters can be fully transferable.