Recently, the growing memory demands of embedding tables in Deep Learning
Recommendation Models (DLRMs) pose great challenges for model training and …

The growing memory demands of embedding tables in Deep Learning Recommendation
Models (DLRMs) pose great challenges for model training and deployment. Existing …

Data analysis focuses on harnessing advanced statistics, programming, and machine
learning techniques to extract valuable insights from vast datasets. An increasing volume …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

In current deep learning tasks, Adam style optimizers such as Adam, Adagrad, RMSProp,
Adafactor, and Lion have been widely used as alternatives to SGD style optimizers. These …

Retrieval augmented generation has emerged as an effective method to enhance large
language model performance. This approach typically relies on an internal retrieval module …