NeuMiss is a neural network architecture aimed at handling missing values, usually used as a preprocessing layer.
For a detailed description of the problem of encoding dirty categorical data, see NeuMiss networks: differentiable programming for supervised learning with missing values [1] and What’s a good imputation to predict with missing values? [2].
| [1] | Marine Le Morvan, et al. NeuMiss networks: differentiable programming for supervised learning with missing values. 2020. Advances in Neural Information Processing Systems 33 (NeurIPS 2020) |
| [2] | Marine Le Morvan, et al. What’s a good imputation to predict with missing values. 2021. |