If there’s one thing that has fueled the rapid progress of AI and machine learning (ML), it’s data.Without high-quality labeled datasets, modern supervised learning systems simply wouldn’t ...

They found that there’s “heavy borrowing” of datasets in machine learning — e.g., a community working on one task might borrow a dataset created for another task — raising concerns about ...

A team led by computer scientists from MIT examined ten of the most-cited datasets used to test machine learning systems. They found that around 3.4 percent of the data was inaccurate or ...

Our understanding of progress in machine learning has been colored by flawed testing data. The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it ...

Training data refers to large datasets to teach machine learning models. It is essential for machine learning algorithms to achieve their objectives. In supervised learning, the algorithm looks at ...

Semisupervised deep learning (SSDL) is a popular strategy to leverage unlabeled data for machine learning when labeled data is not readily available. In real-world scenarios, different unlabeled data ...

If the datasets used to train machine-learning models contain biased data, it is likely the system could exhibit that same bias when it makes decisions in practice.

Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical modeling. However, expensive and ...

Differential privacy is a method for protecting people’s privacy when their data is included in large datasets. Because differential privacy limits how much the machine learning model can depend ...