Open problems in machine learning | Amazon Science

October 2021, Rama Chellapa, a Bloomberg Distinguished Professor in the Departments of Electrical and Computer Engineering and Biomedical Engineering at Johns Hopkins University, gave a keynote presentation at Amazon's annual machine learning conference. Learn more: https://www.amazon.science/videos-webinars/amazons-annual-machine-learning-conference-featured-presentations-from-thought-leaders-within-academia Rama discusses his group's recent works on building operational systems for face recognition and action recognition using deep learning. While reasonable success can be claimed, many open problems still remain to be addressed. These include bias detection and mitigation, domain adaptation and generalization, learning from unlabeled data, handling adversarial attacks, and selecting the best subsets of training data in mini-batch learning. Some of Rama's recent works addressing these challenges will be summarized. Follow us: Website: https://www.amazon.science Twitter: https://twitter.com/AmazonScience Facebook: https://www.facebook.com/AmazonScience Instagram: https://www.instagram.com/AmazonScience LinkedIn: https://www.linkedin.com/showcase/AmazonScience Newsletter: https://www.amazon.science/newsletter #AmazonScience #MachineLearning