Data poisoning or model poisoning attacks involve polluting a machine learning model’s training data. Data poisoning is considered an integrity attack because tampering with the training data ...

Machine learning depends on a number of algorithms for turning a data set into a model. Which algorithm works best depends on the kind of problem you’re solving, the computing resources ...

More recently GPT-3, a language model that uses ... transfer learning has received relatively little visibility. While many machine learning experts and data scientists are likely familiar with ...

The nonprofit Coalition for Health AI has released details of its long-discussed model card registry, a central repository for AI ... of a model’s training data, fairness metrics and intended ...

Missing data, however, means that the data points are unknown. There are several problems in using sparse data to train a machine learning model. If the data is too sparse, it can increase the ...

of data points. For many machine learning tasks, this is more than enough. To increase beyond that, you can use techniques like stochastic gradient descent and update the machine learning model in ...

The accelerating power of machine learning in diagnosing disease and in sorting and classifying health data will empower ... size of the image repository A self-supervised model trained on chest ...

This works for tasks such as classifying images but fails on sequential data such as text. A machine learning model that processes text must not only compute every word but also take into ...

"Tanjo's machine learning technology allows us to improve our collective intelligence while simultaneously mitigating otherwise tedious and costly tasks associated with data-mining." Parker said the ...