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Found 5 papers, 0 papers with code
Improving generalization in large language models by learning prefix subspaces
We show in this paper that "Parameter Efficient Fine-Tuning" (PEFT) methods, however, are perfectly compatible with this original approach, and propose to learn entire simplex of continuous prefixes.
Learning a binary search with a recurrent neural network. A novel approach to ordinal regression analysis
Deep neural networks are a family of computational models that are naturally suited to the analysis of hierarchical data such as, for instance, sequential data with the use of recurrent neural networks.
Neural translation and automated recognition of ICD10 medical entities from natural language
This article investigates the applications of deep neural sequence models to the medical entity recognition from natural language problem.
A deep artificial neural network based model for underlying cause of death prediction from death certificates
Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software.
Deep clustering of longitudinal data
These methods provide a framework to model complex, non-linear interactions in large datasets, and are naturally suited to the analysis of hierarchical data such as, for instance, longitudinal data with the use of recurrent neural networks.
