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Lost in Pruning: The Effects of Pruning Neural Networks beyond Test
  Accuracy

Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy

4 March 2021
Lucas Liebenwein
Cenk Baykal
Brandon Carter
David K Gifford
Daniela Rus
    AAML
ArXivPDFHTML

Papers citing "Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy"

12 / 12 papers shown
Title
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
Boqian Wu
Q. Xiao
Shunxin Wang
N. Strisciuglio
Mykola Pechenizkiy
M. V. Keulen
D. Mocanu
Elena Mocanu
OOD
3DH
52
0
0
03 Oct 2024
Compress and Compare: Interactively Evaluating Efficiency and Behavior
  Across ML Model Compression Experiments
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments
Angie Boggust
Venkatesh Sivaraman
Yannick Assogba
Donghao Ren
Dominik Moritz
Fred Hohman
VLM
50
3
0
06 Aug 2024
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
Chenghao Fan
Zhenyi Lu
Wei Wei
Jie Tian
Xiaoye Qu
Dangyang Chen
Yu Cheng
MoMe
44
5
0
17 Jun 2024
OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the
  Memory Usage of Neural Networks
OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks
Benoit Steiner
Mostafa Elhoushi
Jacob Kahn
James Hegarty
26
8
0
24 Oct 2022
On the Robustness and Anomaly Detection of Sparse Neural Networks
On the Robustness and Anomaly Detection of Sparse Neural Networks
Morgane Ayle
Bertrand Charpentier
John Rachwan
Daniel Zügner
Simon Geisler
Stephan Günnemann
AAML
50
3
0
09 Jul 2022
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
Edouard Yvinec
Arnaud Dapogny
Matthieu Cord
Kévin Bailly
40
9
0
08 Jul 2022
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
Zhengqi He
Zeke Xie
Quanzhi Zhu
Zengchang Qin
67
27
0
17 Jun 2022
Recall Distortion in Neural Network Pruning and the Undecayed Pruning
  Algorithm
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Aidan Good
Jia-Huei Lin
Hannah Sieg
Mikey Ferguson
Xin Yu
Shandian Zhe
J. Wieczorek
Thiago Serra
19
11
0
07 Jun 2022
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another
  in Neural Networks
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu
Thiago Serra
Srikumar Ramalingam
Shandian Zhe
27
48
0
09 Mar 2022
Sparse Flows: Pruning Continuous-depth Models
Sparse Flows: Pruning Continuous-depth Models
Lucas Liebenwein
Ramin Hasani
Alexander Amini
Daniela Rus
8
16
0
24 Jun 2021
Post-Training Sparsity-Aware Quantization
Post-Training Sparsity-Aware Quantization
Gil Shomron
F. Gabbay
Samer Kurzum
U. Weiser
MQ
31
32
0
23 May 2021
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
178
1,027
0
06 Mar 2020
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