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.