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Fully Decoupled Neural Network Learning Using Delayed Gradients

Fully Decoupled Neural Network Learning Using Delayed Gradients

21 June 2019
Huiping Zhuang
Yi Wang
Qinglai Liu
Shuai Zhang
Zhiping Lin
    FedML
ArXivPDFHTML

Papers citing "Fully Decoupled Neural Network Learning Using Delayed Gradients"

11 / 11 papers shown
Title
PETRA: Parallel End-to-end Training with Reversible Architectures
PETRA: Parallel End-to-end Training with Reversible Architectures
Stéphane Rivaud
Louis Fournier
Thomas Pumir
Eugene Belilovsky
Michael Eickenberg
Edouard Oyallon
25
0
0
04 Jun 2024
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
Adel Nabli
Louis Fournier
Pierre Erbacher
Louis Serrano
Eugene Belilovsky
Edouard Oyallon
FedML
48
1
0
03 Jun 2024
A Survey From Distributed Machine Learning to Distributed Deep Learning
A Survey From Distributed Machine Learning to Distributed Deep Learning
Mohammad Dehghani
Zahra Yazdanparast
23
0
0
11 Jul 2023
ADA-GP: Accelerating DNN Training By Adaptive Gradient Prediction
ADA-GP: Accelerating DNN Training By Adaptive Gradient Prediction
Vahid Janfaza
Shantanu Mandal
Farabi Mahmud
A. Muzahid
19
2
0
22 May 2023
Asynchronous Training Schemes in Distributed Learning with Time Delay
Asynchronous Training Schemes in Distributed Learning with Time Delay
Haoxiang Wang
Zhanhong Jiang
Chao Liu
S. Sarkar
D. Jiang
Young M. Lee
25
2
0
28 Aug 2022
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep
  Learning
Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learning
S. Akintoye
Liangxiu Han
H. Lloyd
Xin Zhang
Darren Dancey
Haoming Chen
Daoqiang Zhang
FedML
34
5
0
22 Jul 2022
Cortico-cerebellar networks as decoupling neural interfaces
Cortico-cerebellar networks as decoupling neural interfaces
J. Pemberton
E. Boven
Richard Apps
Rui Ponte Costa
30
6
0
21 Oct 2021
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on
  Gradient-Free ADMM Framework
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework
Junxiang Wang
Hongyi Li
Zheng Chai
Yongchao Wang
Yue Cheng
Liang Zhao
MQ
21
3
0
20 May 2021
A Hybrid Parallelization Approach for Distributed and Scalable Deep
  Learning
A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning
S. Akintoye
Liangxiu Han
Xin Zhang
Haoming Chen
Daoqiang Zhang
22
15
0
11 Apr 2021
Accumulated Decoupled Learning: Mitigating Gradient Staleness in
  Inter-Layer Model Parallelization
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization
Huiping Zhuang
Zhiping Lin
Kar-Ann Toh
26
4
0
03 Dec 2020
Pipelined Backpropagation at Scale: Training Large Models without
  Batches
Pipelined Backpropagation at Scale: Training Large Models without Batches
Atli Kosson
Vitaliy Chiley
Abhinav Venigalla
Joel Hestness
Urs Koster
35
33
0
25 Mar 2020
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