March 28, 2024, 4:43 a.m. | Hailin Zhang, Zirui Liu, Boxuan Chen, Yikai Zhao, Tong Zhao, Tong Yang, Bin Cui

cs.LG updates on

arXiv:2312.03256v2 Announce Type: replace
Abstract: Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding compression solutions cannot simultaneously meet three key design requirements: memory efficiency, low latency, and adaptability to dynamic data distribution. This paper presents CAFE, a Compact, Adaptive, and Fast Embedding compression framework that addresses the above requirements. The design philosophy of CAFE is to dynamically allocate more memory resources to important features …

abstract adaptability arxiv challenges compact compression cs.lg data deep learning deployment design distribution dynamic efficiency embedding key latency low low latency memory recommendation requirements scale solutions tables training type

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

IT Commercial Data Analyst - ESO

@ National Grid | Warwick, GB, CV34 6DA

Stagiaire Data Analyst – Banque Privée - Juillet 2024

@ Rothschild & Co | Paris (Messine-29)

Operations Research Scientist I - Network Optimization Focus

@ CSX | Jacksonville, FL, United States

Machine Learning Operations Engineer

@ Intellectsoft | Baku, Baku, Azerbaijan - Remote

Data Analyst

@ Health Care Service Corporation | Richardson Texas HQ (1001 E. Lookout Drive)