A modified version of the standard SEIR model that incorporates COVID-infected flights in and out of Canada could enable early detection of outbreaks, more accurately estimate the reproduction number of the disease and better evaluate the impact of travel restrictions and the implications of lifting these measures.
We propose La-MAML, a fast and online meta-learning algorithm for continual learning from streaming data, with inbuilt learning rate modulation.
In a future of single-patient prediction from big biomedical data, it may become central that modeling for inference and modeling for prediction are related but importantly different.
Mila collaborates with Stony Brook Medicine. A team led by Joseph Paul Cohen builds a public dataset and models to predict severity of COVID-19 pneumonia. Initial results are promising and the tools are ready for more evaluation.
Manifold Mixup is a simple and easy-to-implement regularization method that improves generalization of deep learning models.
