In an effort to patch these issues, we integrated a meta-learning technique in order to shift the objective of learning to solve a task into the objective of learning how to learn to solve a task (or a set of tasks), which we empirically show that improves overall stability and performance of Deep RL algorithms.
In this paper we present an extension over the Transformer-block architecture used in neural language models, specifically in GPT2, in order to incorporate entity annotations during training.
Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors.
Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance.
We highlight the advantages and disadvantages of the approaches, the challenges of the task, the lack of agreed-upon standards in the task and propose a way to further expand the boundaries of the field.
Background: In this paper we present the approaches and methods employed in order to deal with a large scale multi-label semantic indexing task of biomedical papers.
The algorithm is hybrid in nature, combining novel and known concepts, such as a logic-based strategy and syntactic text-similarity measures on semantic annotations and textual descriptions.
Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables.
When the prediction targets are binary the task is called multi-label classification, while when the targets are continuous the task is called multi-target regression.