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Found 4 papers, 1 papers with code
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
We show that the model performs with high reliability and efficiency on the online CPD problem ($\sim$98\% and $\sim$97\% area under precision-recall curve respectively).
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation
Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties.
De Novo Molecular Generation with Stacked Adversarial Model
A second stage model then takes these features to learn properties of the molecules and refine more valid molecules.
Pandemic model with data-driven phase detection, a study using COVID-19 data
The main contributions of our model are: (a) providing interpretation of the parameters, (b) determining which parameters of the model are more important to produce changes in the spread of the disease, and (c) using data-driven discovery of sudden changes in the evolution of the pandemic.
