Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature.
We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
4 code implementations • • Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs.