394 citations · 1.1k across the 21 of their papers we have counts for
4 papers · 1 filter
MTrainS: Improving DLRM training efficiency using heterogeneous memories
Hiwot Tadese Kassa, Paul Johnson, Jason Akers +7
Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires…
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Buyun Zhang, Liang Luo, Xi Liu +14
Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architec…
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Xiaocong Du, Bhargav Bhushanam, Jiecao Yu +7
Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant…
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi +21
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks…