An extensive study is conducted that considers 12 experimental designs, 7 families of predictivemodels, 7 test functions that emulate physical processes, and 8 noise settings, both homoscedastic and heteroscedastic.
The results of the research can have an immediate impact on the work of practitioners, providing guidelines for practical applications of design of experiments (doe) and machine learning (ml) as a methodology to collect and analyze data on a specific industrial phenomenon.
Machine learning techniques for natural images are ill-equipped to deal with pathology images that are significantly large and noisy, require expensive labeling, are hard to interpret, and are susceptible to spurious correlations.
We propose a set of practical guidelines for machine learning evaluation in pathology that address the above concerns.
We study trends in model size of notable machine learning systems over time using a curated dataset.
From 1950 to 2018, model size in language models increased steadily by seven orders of magnitude, then accelerated, with model size increasing by another five orders of magnitude in just 4 yearsfrom 2018 to 2022.
In this survey, some of the most recent advances of data cleaning approaches are examined for their effectiveness and the future research directions are suggested to close the gap in each of the methods.
We study whether a machine learning model can be effectively trained to accurately disambiguate between original human and seemingly human (that is, chatgpt-generated) text, especially when this text is short.
Our study focuses on short online reviews, conducting two experiments comparing human-generated and chatgpt-generated text.