Accuracy metrics analysis to identify applicability scope and discuss explainability and interpretability of the resulting values
The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities.
Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear.
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms.
Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets.
However, there exist only few metrics for the accuracy measurement of overlapping and multi-resolution clustering algorithms on large datasets.
While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings.
This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budget.