AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing n ....AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.Read moreRead less
Towards equity in crash protection. Women are at increased relative risk for death and serious injury in motor vehicle crashes compared to men and the reasons for this are not clear. This Fellowship aims to build a new model that describes the mechanistic pathways for this inequity to identify where and how intervention could reduce this relative risk. This will establish what population groups have good and poor access to the best vehicle safety technologies, the differences, and what might cau ....Towards equity in crash protection. Women are at increased relative risk for death and serious injury in motor vehicle crashes compared to men and the reasons for this are not clear. This Fellowship aims to build a new model that describes the mechanistic pathways for this inequity to identify where and how intervention could reduce this relative risk. This will establish what population groups have good and poor access to the best vehicle safety technologies, the differences, and what might cause these differences in the benefits of vehicle safety technology between women and men. The outcomes will be of use to academics, policy makers and industry designing to new ways to protect women in crashes and close this gender gap.Read moreRead less