Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance ...
Treating annotation as a data understanding problem, rather than a labeling workflow challenge, can systematically drive down error rates and reduce the time and cost of producing high-quality data ...
The study demonstrates machine learning's role in predicting compressive strength of rice husk ash concrete, aiding the shift ...
Research shows how artificial intelligence is revolutionizing plastics manufacturing through material development and process ...
By using reinforcement learning, researchers train virtual agent to determine the best time to administer medication based on ...
The applied mathematician and Ramsey Theory Group founder launches a mission to reshape how organizations understand and trust AI.
Inspired by how the human brain consolidates memory, the 'Nested Learning' framework allows different parts of a model to ...
Monoclonal antibody (mAb) manufacturing must continually improve to keep up with increasing demands. To do this, biomanufacturers can deploy machine learning tools to augment traditional process ...
The project will build upon CSIRO’s expertise in the field of QML to develop new and innovative QML models. QML has the potential to offer enhanced reliability, training speed-up and unique feature ...
In both cases, it would be better to train the machine learning model with a loss function that ignores the human’s objective and then adjust predictions ex post according to that objective. We ...
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