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Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks.
Sipping neural networks: Sensitivity-informed provable pruning of neural networks
Baykal, C., Liebenwein, L., Gilitschenski, I., Feldman, D., and Rus, D · 1910
Earlier work this paper cites.
80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T · 2008
Earlier work this paper cites.
Semantic contours from inverse detectors
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., and Malik, J · 2011
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2015
Earlier work this paper cites.
Han, S., Mao, H., and Dally, W. J · 2015
Earlier work this paper cites.
In search of the real inductive bias: On the role of implicit regularization in deep learning
Neyshabur, B., Tomioka, R., and Srebro, N · 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.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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Rethinking atrous convolution for semantic image segmentation
Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Earlier work this paper cites.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M. and Gupta, S · 2017
Earlier work this paper cites.
Stronger generalization bounds for deep nets via a compression approach
Arora, S., Ge, R., Neyshabur, B., and Zhang, Y · 2018
Earlier work this paper cites.
Stochastic activation pruning for robust adversarial defense
Dhillon, G. S., Azizzadenesheli, K., Bernstein, J. D., Kossaifi, J., Khanna, A., Lipton, Z. C., and Anandkumar, A · 2018
Earlier work this paper cites.
Sparse dnns with improved adversarial robustness
Guo, Y., Zhang, C., Zhang, C., and Chen, Y · 2018
Earlier work this paper cites.
Soft filter pruning for accelerating deep convolutional neural networks
He, Y., Kang, G., Dong, X., Fu, Y., and Yang, Y · 2018
Cited alongside, same era.
Autopruner: An end-to-end trainable filter pruning method for efficient deep model inference
Luo, J.-H. and Wu, J · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Towards understanding the role of over-parametrization in generalization of neural networks
Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N · 2018
Cited alongside, same era.
Do cifar-10 classifiers generalize to cifar-10?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
Rethinking the value of network pruning
Liu, Z., Sun, M., Zhou, T., Huang, G., and Darrell, T · 2019
Later among the works it cites.
Benchmarking robustness in object detection: Autonomous driving when winter is coming
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A. S., Bethge, M., and Brendel, W · 2019
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Deterministic PAC-bayesian generalization bounds for deep networks via generalizing noise-resilience
Nagarajan, V. and Kolter, Z · 2019
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The role of over-parametrization in generalization of neural networks
Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
Later among the works it cites.
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Cited alongside, same era.
Adversarial robustness of pruned neural networks
Wang, L., Ding, G. W., Huang, R., Cao, Y., and Lui, Y. C · 2018
Cited alongside, same era.
Yu, J., Yang, L., Xu, N., Yang, J., and Huang, T · 2018
Cited alongside, same era.
Zhao, Y., Shumailov, I., Mullins, R., and Anderson, R · 2018
Cited alongside, same era.
Compressibility and generalization in large-scale deep learning
Zhou, W., Veitch, V., Austern, M., Adams, R. P., and Orbanz, P · 2018
Cited alongside, same era.
Learning and generalization in overparameterized neural networks, going beyond two layers
Allen-Zhu, Z., Li, Y., and Liang, Y · 2019
Cited alongside, same era.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., and Katz, B · 2019
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Belkin, M., Hsu, D., Ma, S., and Mandal, S · 2019
Cited alongside, same era.
Sehwag, V., Wang, S., Mittal, P., and Jana, S · 2019
Later among the works it cites.
Towards robust compressed convolutional neural networks
Wijayanto, A. W., Choong, J. J., Madhawa, K., and Murata, T · 2019
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Adversarial robustness vs. model compression, or both
Ye, S., Xu, K., Liu, S., Cheng, H., Lambrechts, J.-H., Zhang, H., Zhou, A., Ma, K., Wang, Y., and Lin, X · 2019
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Large batch optimization for deep learning: Training bert in 76 minutes
You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., and Hsieh, C.-J · 2019
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What is the state of neural network pruning?
Blalock, D., Gonzalez Ortiz, J. J., Frankle, J., and Guttag, J · 2020
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Overinterpretation reveals image classification model pathologies
Carter, B., Jain, S., Mueller, J., and Gifford, D · 2020
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Campfire: Compressible, regularization-free, structured sparse training for hardware accelerators
Gamboa, N., Kudrolli, K., Dhoot, A., and Pedram, A · 2020
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Soft threshold weight reparameterization for learnable sparsity
Kusupati, A., Ramanujan, V., Somani, R., Wortsman, M., Jain, P., Kakade, S., and Farhadi, A · 2020
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Provable filter pruning for efficient neural networks
Liebenwein, L., Baykal, C., Lang, H., Feldman, D., and Rus, D · 2020
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Deep double descent: Where bigger models and more data hurt
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I · 2020
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Comparing fine-tuning and rewinding in neural network pruning
Renda, A., Frankle, J., and Carbin, M · 2020
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Deep latent competition: Learning to race using visual control policies in latent space
Schwarting, W., Seyde, T., Gilitschenski, I., Liebenwein, L., Sander, R., Karaman, S., and Rus, D · 2020
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Hydra: Pruning adversarially robust neural networks
Sehwag, V., Wang, S., Mittal, P., and Jana, S · 2020
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Woodfisher: Efficient second-order approximations for model compression
Singh, S. P. and Alistarh, D · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Tanaka, H., Kunin, D., Yamins, D. L., and Ganguli, S · 2020
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Picking winning tickets before training by preserving gradient flow
Wang, C., Zhang, G., and Grosse, R · 2020
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