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Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation
  System with Non-Stationary Data

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data

16 October 2020
Mao Ye
Dhruv Choudhary
Jiecao Yu
Ellie Wen
Zeliang Chen
Jiyan Yang
Jongsoo Park
Qiang Liu
A. Kejariwal
ArXivPDFHTML

Papers citing "Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data"

5 / 5 papers shown
Title
Enhanced Exploration in Neural Feature Selection for Deep Click-Through
  Rate Prediction Models via Ensemble of Gating Layers
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers
L. Guan
Xia Xiao
Ming-yue Chen
Youlong Cheng
27
1
0
07 Dec 2021
Measuring the Algorithmic Efficiency of Neural Networks
Measuring the Algorithmic Efficiency of Neural Networks
Danny Hernandez
Tom B. Brown
241
94
0
08 May 2020
What is the State of Neural Network Pruning?
What is the State of Neural Network Pruning?
Davis W. Blalock
Jose Javier Gonzalez Ortiz
Jonathan Frankle
John Guttag
191
1,027
0
06 Mar 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using
  Model Parallelism
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
M. Shoeybi
M. Patwary
Raul Puri
P. LeGresley
Jared Casper
Bryan Catanzaro
MoE
245
1,826
0
17 Sep 2019
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Z. Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
716
6,746
0
26 Sep 2016
1