research
∙
05/03/2023
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Sharding a large machine learning model across multiple devices to balan...
share
research
∙
09/02/2022
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is ...
research
∙
08/12/2022
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Embedding learning is an important technique in deep recommendation mode...
research
∙
01/19/2022
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
We devise a performance model for GPU training of Deep Learning Recommen...
research
∙
05/04/2021
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Deep learning recommendation systems at scale have provided remarkable g...
research
∙
10/18/2020
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism
Data Parallelism (DP) and Model Parallelism (MP) are two common paradigm...
research
∙
10/16/2020
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Large scale deep learning provides a tremendous opportunity to improve t...
research
∙
10/12/2017
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data
Ever growing volume and velocity of data coupled with decreasing attenti...
research
∙
08/07/2017
