Traditionally, there are two types of methods in machine learning: supervised and unsupervised. In the first case, the data is pre-labeled and the algorithms are trained based on these labels. In unsupervised methods, algorithms independently identify patterns in the data. But new technology combines the advantages of both approaches, allowing models to learn more flexibly.
The main idea of ContextSSL is to train a context-based model that takes into account the dynamics of the environment. This allows models to adapt their response depending on the situation. For example, the model can predict drug dosages by gender based on analysis of data on the patient’s condition, as well as accurately predict the length of hospital stay, taking into account the factors that influence this.
Using the transformer module, ContextSSL can change the behavior of the model on the fly, making it more responsive to changes in the surrounding context. This opens new possibilities for applications in fields such as computer vision, bioinformatics and medical research.
Source: Ferra
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