Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova… - Advances in neural …, 2016 - proceedings.neurips.cc
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …
convolutional neural networks, a factor that has hindered their deployment in low-power …
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, A Ibraimova… - Advances in Neural …, 2016 - proceedings.neurips.cc
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …
convolutional neural networks, a factor that has hindered their deployment in low-power …
[PDF][PDF] PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, D Vetrov, P Kohli - 2016 - recognition.su
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Page 1
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Michael …
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Michael …
[PDF][PDF] PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, D Vetrov, P Kohli - 2016 - machinelearning.ru
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Page 1
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Michael …
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions Michael …
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, A Ibraimova, D Vetrov, P Kohli - arXiv preprint arXiv …, 2015 - arxiv.org
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …
convolutional neural networks, a factor that has hindered their deployment in low-power …
[PDF][PDF] PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, D Vetrov, P Kohli - researchgate.net
This paper proposes a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in lowpower …
convolutional neural networks, a factor that has hindered their deployment in lowpower …
[PDF][PDF] PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, A Ibraimova, D Vetrov, P Kohli - publications.hse.ru
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in lowpower …
convolutional neural networks, a factor that has hindered their deployment in lowpower …
PerforatedCNNs: Acceleration through elimination of redundant convolutions
M Figurnov, D Vetrov, A Ibraimova, P Kohli - Advances in Neural …, 2016 - elibrary.ru
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …
convolutional neural networks, a factor that has hindered their deployment in low-power …
[PDF][PDF] PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, A Ibraimova, D Vetrov, P Kohli - papers.neurips.cc
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in lowpower …
convolutional neural networks, a factor that has hindered their deployment in lowpower …
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
M Figurnov, D Vetrov, P Kohli - 2016 - openreview.net
We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …
convolutional neural networks, a factor that has hindered their deployment in low-power …