27 citations · 78 across the 12 of their papers we have counts for
3 papers · 1 filter
Detection of data drift and outliers affecting machine learning model performance over time
Samuel Ackerman, Eitan Farchi, Orna Raz +2
A trained ML model is deployed on another `test' dataset where target feature values (labels) are unknown. Drift is distribution change between the training and deployment data, wh…
Sequential Drift Detection in Deep Learning Classifiers
Samuel Ackerman, Parijat Dube, Eitan Farchi
We utilize neural network embeddings to detect data drift by formulating the drift detection within an appropriate sequential decision framework. This enables control of the false…
Improving the affordability of robustness training for DNNs
Sidharth Gupta, Parijat Dube, Ashish Verma
Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational…