Fetching the paper…
Reading the bibliography…
Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network.
Comparing biases for minimal network construction with back-propagation
Hanson, S. J. and Pratt, L. Y · 1989
Earlier work this paper cites.
Optimal brain damage
LeCun, Y., Denker, J. S., and Solla, S. A · 1989
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
Earlier work this paper cites.
Do deep nets really need to be deep?
Ba, L. J. and Caruana, R · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W. J · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
Earlier work this paper cites.
Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2016
Earlier work this paper cites.
Residual networks behave like ensembles of relatively shallow networks
Veit, A., Wilber, M. J., and Belongie, S · 2016
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
Yu, F. and Koltun, V · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
He, Y., Zhang, X., and Sun, J · 2017
Cited alongside, same era.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks
Hoffer, E., Hubara, I., and Soudry, D · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
CondenseNet: An efficient densenet using learned group convolutions
Huang, G., Liu, S., van der Maaten, L., and Weinberger, K. Q · 2018
Closest in time.
Residual connections encourage iterative inference
Jastrzębski, S., Arpit, D., Ballas, N., Verma, V., Che, T., and Bengio, Y · 2018
Closest in time.
Faster gaze prediction with dense networks and Fisher pruning
Theis, L., Korshunova, I., Tejani, A., and Huszár, F · 2018
Closest in time.
Characterising across stack optimisations for deep convolutional neural networks
Turner, J., Cano, J., Radu, V., Crowley, E. J., O’Boyle, M., and Storkey, A · 2018
Closest in time.
NetAdapt: Platform-aware neural network adaptation for mobile applications
Yang, T.-J., Howard, A., Chen, B., Zhang, X., Go, A., Sze, V., and Adam, H · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep roots: Improving CNN efficiency with hierarchical filter groups
Ioannou, Y., Robertson, D., Cipolla, R., and Criminisi, A · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., and Zhang, C · 2017
Cited alongside, same era.
Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
Cited alongside, same era.
SCNN: An accelerator for compressed-sparse convolutional neural networks
Parashar, A., Rhu, M., Mukkara, A., Puglielli, A., Venkatesan, R., Khailany, B., Emer, J., Keckler, S. W., and Dally, W. J · 2017
Cited alongside, same era.
Group sparse regularization for deep neural networks
Scardapane, S., Comminiello, D., Hussain, A., and Uncini, A · 2017
Cited alongside, same era.
Ye, J., Lu, X., Lin, Z., and Wang, J. Z · 2018
Closest in time.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
Closest in time.
Critical learning periods in deep neural networks
Achille, A., Rovere, M., and Soatto, S · 2019
Closest in time.
The lottery ticket hypothesis: Finding small, trainable neural networks
Frankle, J. and Carbin, M · 2019
Closest in time.
The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S · 2019
Closest in time.
Separable layers enable structured efficient linear substitutions
Gray, G., Crowley, E. J., and Storkey, A · 2019
Closest in time.
On the relation between the sharpest directions of DNN loss and the SGD step length
Jastrzębski, S., Kenton, Z., Ballas, N., Fischer, A., Bengio, Y., and Storkey, A · 2019
Closest in time.
SNIP: Single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P. H. S · 2019
Closest in time.