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AI accelerated the search for a coronavirus vaccine, detected Covid-19 cases, and otherwise softened the pandemic’s blow. Machine learning researchers worldwide scrambled to harness the technology against the coronavirus.
AI accelerated the search for a coronavirus vaccine, detected Covid-19 cases, and otherwise softened the pandemic’s blow. Machine learning researchers worldwide scrambled to harness the technology against the coronavirus.
Covid Moonshot, an open-source project to vet potential medicines using machine learning, is closing in on compounds that might help curb Covid-19. Four new antiviral drugs identified by the project are ready to advance to animal trials.
Covid Moonshot, an open-source project to vet potential medicines using machine learning, is closing in on compounds that might help curb Covid-19. Four new antiviral drugs identified by the project are ready to advance to animal trials.
Semi-supervised learning — a set of training techniques that use a small number of labeled examples and a large number of unlabeled examples — typically treats all unlabeled examples the same way. But some examples are more useful for learning than others.
Semi-supervised learning — a set of training techniques that use a small number of labeled examples and a large number of unlabeled examples — typically treats all unlabeled examples the same way. But some examples are more useful for learning than others.
Researchers are drawing up blueprints for drugs to fight Covid-19. Machine learning is identifying those most likely to be effective. Covid Moonshot, an international group of scientists in academia and industry, is crowdsourcing designs for molecules with potential to thwart the coronavirus.
Researchers are drawing up blueprints for drugs to fight Covid-19. Machine learning is identifying those most likely to be effective. Covid Moonshot, an international group of scientists in academia and industry, is crowdsourcing designs for molecules with potential to thwart the coronavirus.
A simple linear classifier paired with a self-supervised feature extractor outperformed a supervised deep learning model on ImageNet, according to new research.
A simple linear classifier paired with a self-supervised feature extractor outperformed a supervised deep learning model on ImageNet, according to new research.
In small data settings where labels are scarce, semi-supervised learning can train models by using a small number of labeled examples and a larger set of unlabeled examples. A new method outperforms earlier techniques.
In small data settings where labels are scarce, semi-supervised learning can train models by using a small number of labeled examples and a larger set of unlabeled examples. A new method outperforms earlier techniques.
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