The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

Building a scalable and real-time recommendation system is vital for many businesses
driven by time-sensitive customer feedback, such as short-videos ranking or online ads …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …

Scaling laws play an instrumental role in the sustainable improvement in model quality.
Unfortunately, recommendation models to date do not exhibit such laws similar to those …

Deep learning recommendation models (DLRMs) are using increasingly larger embedding
tables to represent categorical sparse features such as video genres. Each embedding row …

Existing benchmark datasets for recommender systems (RS) either are created at a small
scale or involve very limited forms of user feedback. RS models evaluated on such datasets …

The proliferation of pre-trained models (PTMs) and datasets has led to the emergence of
centralized model hubs like Hugging Face, which facilitate collaborative development and …

We study a mismatch between the deep learning recommendation models' flat architecture,
common distributedtraining paradigm and hierarchical data center topology. To address the …