2 papers
cs.IR2022
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions
Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli +4
A key characteristic of deep recommendation models is the immense memory requirements of their embedding tables. These embedding tables can often reach hundreds of gigabytes which…
cs.LG2018
Automatic Rule Learning for Autonomous Driving Using Semantic Memory
Dmitriy Korchev, Aruna Jammalamadaka, Rajan Bhattacharyya
This paper presents a novel approach for automatic rule learning applicable to an autonomous driving system using real driving data.