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Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices.
Optimal Brain Damage , pp. 598–605
Cun, Y. L., Denker, J. S., and Solla, S. A · 1990
Earlier work this paper cites.
Optimal brain surgeon: Extensions and performance comparisons
Hassibi, B., Stork, D. G., Wolff, G., and Watanabe, T · 1993
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
An on-chip learning neural network
Bo, G. M., Caviglia, D. D., and Valle, M · 2000
Earlier work this paper cites.
Mixed analogue-digital artificial-neural-network architecture with on-chip learning
Schmid, A., Leblebici, Y., and Mlynek, D · 2000
Earlier work this paper cites.
A mathematical theory of communication
Shannon, C. E · 2001
Earlier work this paper cites.
The im algorithm: a variational approach to information maximization
Barber, D. and Agakov, F. V · 2003
Earlier work this paper cites.
Model compression
Bucilua, C., Caruana, R., and Niculescu-Mizil, A · 2006
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Mnist handwritten digit database, 2010
LeCun, Y., Cortes, C., and Burges, C · 2010
Earlier work this paper cites.
Phase change memory
Wong, H.-S. P., Raoux, S., Kim, S., Liang, J., Reifenberg, J. P., Rajendran, B., Asheghi, M., and Goodson, K. E · 2010
Earlier work this paper cites.
Large scale distributed deep networks
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K., et al · 2012
Earlier work this paper cites.
The design of rate-compatible protograph ldpc codes
Van Nguyen, T., Nosratinia, A., and Divsalar, D · 2012
Earlier work this paper cites.
Capacity optimization of emerging memory systems: A shannon-inspired approach to device characterization
Engel, J. H., Eryilmaz, S. B., Kim, S., BrightSky, M., Lam, C., Lung, H., Olshausen, B. A., and Wong, H. . P · 2014
Earlier work this paper cites.
New insights and perspectives on the natural gradient method
Martens, J · 2014
Earlier work this paper cites.
An exploration of parameter redundancy in deep networks with circulant projections
Cheng, Y., Yu, F. X., Feris, R. S., Kumar, S., Choudhary, A., and Chang, S.-F · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
Earlier work this paper cites.
Precise neural network computation with imprecise analog devices
Binas, J., Neil, D., Indiveri, G., Liu, S.-C., and Pfeiffer, M · 2016
Earlier work this paper cites.
A kronecker-factored approximate fisher matrix for convolution layers
Grosse, R. and Martens, J · 2016
Earlier work this paper cites.
Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., and Chen, Y · 2016
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.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A · 2016
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Phase-change memory—towards a storage-class memory
Fong, S. W., Neumann, C. M., and Wong, H.-S. P · 2017
Cited alongside, same era.
Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
Cited alongside, same era.
Scalable methods for 8-bit training of neural networks
Banner, R., Hubara, I., Hoffer, E., and Soudry, D · 2018
Cited alongside, same era.
Model compression and acceleration for deep neural networks: The principles, progress, and challenges
Cheng, Y., Wang, D., Zhou, P., and Zhang, T · 2018
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
Later among the works it cites.
rtop-k: A statistical estimation approach to distributed sgd
Barnes, L. P., Inan, H. A., Isik, B., and Özgür, A · 2020
Later among the works it cites.
Universal deep neural network compression
Choi, Y., El-Khamy, M., and Lee, J · 2020
Later among the works it cites.
Model compression and hardware acceleration for neural networks: A comprehensive survey
Deng, L., Li, G., Han, S., Shi, L., and Xie, Y · 2020
Later among the works it cites.
Benchmarking inference performance of deep learning models on analog devices
Fagbohungbe, O. and Qian, L · 2020
Later among the works it cites.
Training with quantization noise for extreme model compression
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Du, Y., Du, L., Gu, X., Du, J., Wang, X. S., Hu, B., Jiang, M., Chen, X., Iyer, S. S., and Chang, M.-C. F · 2018
Cited alongside, same era.
Towards generalization guarantees for sgd: Data-dependent pac-bayes priors
Dziugaite, G. K., Arpino, G., and Roy, D. M · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2018
Cited alongside, same era.
Adaptive quantization of neural networks
Khoram, S. and Li, J · 2018
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P. H · 2018
Cited alongside, same era.
Model compression via distillation and quantization
Polino, A., Pascanu, R., and Alistarh, D · 2018
Cited alongside, same era.
Weightless: Lossy weight encoding for deep neural network compression
Reagan, B., Gupta, U., Adolf, B., Mitzenmacher, M., Rush, A., Wei, G.-Y., and Brooks, D · 2018
Cited alongside, same era.
Fan, A., Stock, P., Graham, B., Grave, E., Gribonval, R., Jégou, H., and Joulin, A · 2020
Later among the works it cites.
Characterising bias in compressed models
Hooker, S., Moorosi, N., Clark, G., Bengio, S., and Denton, E · 2020
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Accurate deep neural network inference using computational phase-change memory
Joshi, V., Gallo, M. L., Haefeli, S., Boybat, I., Nandakumar, S., Piveteau, C., Dazzi, M., Rajendran, B., Sebastian, A., and Eleftheriou, E · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 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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Deepcabac: A universal compression algorithm for deep neural networks
Wiedemann, S., Kirchhoffer, H., Matlage, S., Haase, P., Marban, A., Marinč, T., Neumann, D., Nguyen, T., Schwarz, H., Wiegand, T., Marpe, D., and Samek, W · 2020
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Self-training with noisy student improves imagenet classification
Xie, Q., Luong, M.-T., Hovy, E., and Le, Q. V · 2020
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Transform quantization for cnn compression
Young, S. I., Zhe, W., Taubman, D., and Girod, B · 2020
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Zhou, C., Kadambi, P., Mattina, M., and Whatmough, P. N · 2020
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3d scene compression through entropy penalized neural representation functions
Bird, T., Ballé, J., Singh, S., and Chou, P. A · 2021
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Neural 3d scene compression via model compression
Isik, B · 2021
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Dnr: A tunable robust pruning framework through dynamic network rewiring of dnns
Kundu, S., Nazemi, M., Beerel, P. A., and Pedram, M · 2021
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Information contraction in noisy binary neural networks and its implications
Zhou, C., Zhuang, Q., Mattina, M., and Whatmough, P. N · 2021
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Learning under storage and privacy constraints
Isik, B. and Weissman, T · 2022
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An information-theoretic justification for model pruning
Isik, B., Weissman, T., and No, A · 2022
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Efficient federated random subnetwork training
Pase, F., Isik, B., Gunduz, D., Weissman, T., and Zorzi, M · 2022
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Sparse random networks for communication-efficient federated learning
Isik, B., Pase, F., Gunduz, D., Weissman, T., and Michele, Z · 2023
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Author correction: Analog coding in emerging memory systems
Zarcone, R. V., Engel, J. H., Eryilmaz, S. B., Wan, W., Kim, S., BrightSky, M., Lam, C., Lung, H.-L., Olshausen, B. A., and Wong, H.-S. P · 2045
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