Search Results for author:
Found 2 papers, 0 papers with code
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
MEDA minimizes overfitting by reducing the dependency of the embedding layer on subsequent training data or the Multi-Layer Perceptron (MLP) layers, and achieves data augmentation through training the MLP with varied embedding spaces.
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
However, it is still unknown whether a multi-epoch training paradigm could achieve better results, as the best performance is usually achieved by one-epoch training.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
