From c5bcd9722627a4bfa4caeccdbb32e694d9d328f2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Aur=C3=A9lien?= <aurelien.bellet@inria.fr>
Date: Fri, 2 Apr 2021 09:02:41 +0200
Subject: [PATCH] small fixes in refs
---
main.bib | 24 ++++++------------------
main.tex | 2 +-
2 files changed, 7 insertions(+), 19 deletions(-)
diff --git a/main.bib b/main.bib
index 4050742..52b6cbd 100644
--- a/main.bib
+++ b/main.bib
@@ -605,12 +605,11 @@ pages={211-252}
year={2010}
}
-@incollection{lian2017d-psgd,
+@inproceedings{lian2017d-psgd,
title = {{Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent}},
author = {Lian, Xiangru and Zhang, Ce and Zhang, Huan and Hsieh, Cho-Jui and Zhang, Wei and Liu, Ji},
- booktitle = {Advances in Neural Information Processing Systems},
- year = {2017},
- publisher = {Curran Associates, Inc.},
+ booktitle = {NIPS},
+ year = {2017}
}
@article{nedic2016sgp,
@@ -674,8 +673,7 @@ pages={211-252}
title = {{$D^2$: Decentralized Training over Decentralized Data}},
author = {Tang, Hanlin and Lian, Xiangru and Yan, Ming and Zhang, Ce and Liu, Ji},
booktitle = {ICML},
- year = {2018},
- publisher = {PMLR}
+ year = {2018}
}
@article{xiao2007distributed,
@@ -721,7 +719,7 @@ pages={211-252}
title={Small worlds: The dynamics of networks between order and randomness},
author={Watts, Duncan J},
year={2000},
- publisher={Princeton University Press Princeton}
+ publisher={Princeton University Press}
}
% Random Model Walk !!!
@@ -790,15 +788,6 @@ pages={211-252}
primaryClass={cs.CV}
}
-@misc{kong2021consensus,
- title={Consensus Control for Decentralized Deep Learning},
- author={Lingjing Kong and Tao Lin and Anastasia Koloskova and Martin Jaggi and Sebastian U. Stich},
- year={2021},
- eprint={2102.04828},
- archivePrefix={arXiv},
- primaryClass={cs.LG}
-}
-
@article{krizhevsky2009learning,
title={{Learning Multiple Layers of Features from Tiny Images}},
author={Krizhevsky, Alex},
@@ -832,8 +821,7 @@ pages={211-252}
title = {On the importance of initialization and momentum in deep learning},
author = {Ilya Sutskever and James Martens and George Dahl and Geoffrey Hinton},
booktitle = {ICML},
- year = {2013},
- publisher = {PMLR}
+ year = {2013}
}
@article{lecun1998gradient,
diff --git a/main.tex b/main.tex
index 682998c..c5c3f98 100644
--- a/main.tex
+++ b/main.tex
@@ -807,7 +807,7 @@ We have proposed D-Cliques, a sparse topology that recovers the convergence spee
\begin{itemize}
\item Clustering does not seem to make a difference in MNIST, even when using a higher-capacity model (LeNet) instead of a linear model. (Fig.\ref{fig:d-cliques-mnist-comparison-to-non-clustered-topologies})
- \item Except for the random 10 topology, convergence speed seems to be correlated with scattering in CIFAR-10 with LeNet model (Fig.\ref{fig:d-cliques-cifar10-linear-comparison-to-non-clustered-topologies}). There is also more difference between topologies both in convergence speed and scattering than for MNIST (Fig.~\ref{fig:d-cliques-mnist-comparison-to-non-clustered-topologies}). Scattering computed similar to Consensus Control for Decentralized Deep Learning~\cite{kong2021consensus}.
+ \item Except for the random 10 topology, convergence speed seems to be correlated with scattering in CIFAR-10 with LeNet model (Fig.\ref{fig:d-cliques-cifar10-linear-comparison-to-non-clustered-topologies}). There is also more difference between topologies both in convergence speed and scattering than for MNIST (Fig.~\ref{fig:d-cliques-mnist-comparison-to-non-clustered-topologies}). Scattering computed similar to Consensus Control for Decentralized Deep Learning~\cite{consensus_distance}.
\end{itemize}
\end{document}
--
GitLab