Arun Kejariwal | DeepAI

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∙ 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...

4 Daochen Zha, et al. ∙

∙ 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 ...

15 Mao Ye, et al. ∙

∙ 08/12/2022

AutoShard: Automated Embedding Table Sharding for Recommender Systems

Embedding learning is an important technique in deep recommendation mode...

9 Daochen Zha, et al. ∙

∙ 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...

7 Zhongyi Lin, et al. ∙

∙ 05/04/2021

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

Deep learning recommendation systems at scale have provided remarkable g...

15 Xiaocong Du, et al. ∙

∙ 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...

8 Vipul Gupta, et al. ∙

∙ 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...

16 Mao Ye, et al. ∙

∙ 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...

0 Dhruv Choudhary, et al. ∙

∙ 08/07/2017

Real Time Analytics: Algorithms and Systems

Velocity is one of the 4 Vs commonly used to characterize Big Data. In t...

0 Arun Kejariwal, et al. ∙

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