13 citations · 13 across the 4 of their papers we have counts for
4 papers
Bending the Scaling Law Curve in Large-Scale Recommendation Systems
Qin Ding, Kevin Course, Linjian Ma +19
Learning from user interaction history through sequential models has become a cornerstone of large-scale recommender systems. Recent advances in large language models have revealed…
Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression
Hao Feng, Boyuan Zhang, Fanjiang Ye +9
DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitate…
Nimble GNN Embedding with Tensor-Train Decomposition
Chunxing Yin, Da Zheng, Israt Nisa +3
This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario whe…
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models
Chunxing Yin, Bilge Acun, Xing Liu +1
The memory capacity of embedding tables in deep learning recommendation models (DLRMs) is increasing dramatically from tens of GBs to TBs across the industry. Given the fast growth…