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Geometric algorithms for predicting resilience and recovering damage in neural networks

Geometric algorithms for predicting resilience and recovering damage in neural networks

G. Raghavan
Jiayi Li
Matt Thomson
    AAML
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Papers citing "Geometric algorithms for predicting resilience and recovering damage in neural networks"

3 / 3 papers shown
Title
On the importance of single directions for generalization
On the importance of single directions for generalization
Ari S. Morcos
David Barrett
Neil C. Rabinowitz
M. Botvinick
64
333
0
19 Mar 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
210
3,457
0
09 Mar 2018
Learning both Weights and Connections for Efficient Neural Networks
Learning both Weights and Connections for Efficient Neural Networks
Song Han
Jeff Pool
J. Tran
W. Dally
CVBM
294
6,660
0
08 Jun 2015

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