Therefore, how to identify significantly enriched functions among the set of lncRNAs is an important downstream task for interpreting high-throughput experimental data.
Secondly, given a set of human lncRNA genes such as differential lncRNAs between cancer and normal samples, it is an important downstream task to identify significantly enriched function terms.
Thus, swapping out one task in a workflow will require rewriting not only this task but also all downstream tasks (illustrated in Fig. 4).
We demonstrate how we transfer supervision from RGB images to depth images as obtained from a range sensors, such as the Microsoft Kinect, for the downstream task of saliency detection.
Commenting in more detail we see in a how every parameter definition has to be repeated for every downstream task, from where it was first defined.
Does the downstream task define the relevance of the disentanglement to learn?
This can be exemplified by the implementation of a Luigi task in Fig. 4, lines 19 and 23, where we see that in the run method of the downstream task, there is navigation code tied to the structure of an upstream task. 3.
In other words, the parameter value will need to be passed on all the way from the most downstream task of the workflow up to where it is actually used.
The standard benchmarks for this task do not resemble realistic use cases, and generally assume that downstream task models are built using logistic regression, rather than the complex neural network architectures that are in fact widely employed for language understanding tasks.
We propose evaluating word embeddings by using them as features in simple models for a suite of popular downstream tasks.
When developing workflows, these output methods are connected to the input fields of downstream tasks with a syntax similar to variable assignment.
