15 citations · 21 across the 4 of their papers we have counts for
4 papers
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
Zhaoxia, Deng, Jongsoo Park +17
Tremendous success of machine learning (ML) and the unabated growth in ML model complexity motivated many ML-specific designs in both CPU and accelerator architectures to speed up…
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…
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Mao Ye, Dhruv Choudhary, Jiecao Yu +6
Large scale deep learning provides a tremendous opportunity to improve the quality of content recommendation systems by employing both wider and deeper models, but this comes at gr…
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data
Dhruv Choudhary, Arun Kejariwal, Francois Orsini
Ever growing volume and velocity of data coupled with decreasing attention span of end users underscore the critical need for real-time analytics. In this regard, anomaly detection…