Rough Transformers for Continuous and Efficient Time-Series Modelling
Rough Transformers for Continuous and Efficient Time-Series Modelling
Time-series data in real-world medical settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In such contexts, traditional sequence-based recurrent models struggle. To overcome this, researchers replace recurrent architectures with Neural ODE-based models to model irregularly sampled data and use Transformer-based architectures to account for long-range dependencies. Despite …