Semi-supervised learning uses both tagged and untagged data to fit a model. In some cases, such as Alexa’s, adding the untagged data actually improves the accuracy of the model.

Semi-supervised learning is also applicable to real-world problems where a small amount of labeled data would prevent supervised learning algorithms from functioning.

Deep learning based semi-supervised learning algorithms have shown promising results in recent years. However, they are not yet practical in real semi-supervised learning scenarios, such as ...

Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth taking a step back to look at the gamut of AI algorithms and their uses.After ...

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.

In a similar fashion, ML algorithms learn to fill in the gaps using semi-supervised learning. ML algorithms trained using self-supervised learning seem to pick up on common human cues and are able to ...