Search Results for author:
Found 2 papers, 2 papers with code
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
Using a generated synthetic email promo dataset, we demonstrate similar prediction accuracies for (a) a wide and deep network that takes identifying information (or other categorical features) as input to the wide part and (b) a deep-only neural network that includes embeddings of categorical features in the input.
Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection
Anomaly detection is concerned with identifying examples in a dataset that do not conform to the expected behaviour.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
