Discussing the article: "Neural Networks Made Easy (Part 83): The "Conformer" Spatio-Temporal Continuous Attention Transformer Algorithm"

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Check out the new article: Neural Networks Made Easy (Part 83): The "Conformer" Spatio-Temporal Continuous Attention Transformer Algorithm.

This article introduces the Conformer algorithm originally developed for the purpose of weather forecasting, which in terms of variability and capriciousness can be compared to financial markets. Conformer is a complex method. It combines the advantages of attention models and ordinary differential equations.

The unpredictability of financial market behavior can probably be compared to the volatility of the weather. However, humanity has done quite a lot in the field of weather forecasting. So, we can now quite trust the weather forecasts provided by meteorologists. Can we use their developments to forecast the "weather" in financial markets? In this article, we will get acquainted with the complex algorithm of the "Conformer" Spatio-Temporal Continuous Attention Transformer, which was developed for the purposes of weather forecasting and is presented in the paper "Conformer: Embedding Continuous Attention in Vision Transformer for Weather Forecasting". In their work, the authors of the method propose the Continuous Attention algorithm. They combine it with those we discussed in the previous article on Neural ODE.

Author: Dmitriy Gizlyk