RE•WORK Deep Learning Summit

Most progress in machine learning has been measured according to gains in test-set accuracy on tasks like image recognition. However, test-set accuracy appears to be poorly correlated with other design objectives such as interpretability, robustness to adversarial attacks or training compact networks that can be used in resource constrained environments. This talk will ask whether it is possible to have it all, and more importantly how do we measure progress when we want to train model functions that fulfill multiple criteria.