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We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off.
Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proceedings of the IRE 40
1952
Earlier work this paper cites.
Rissanen, J., Langdon, G.: Universal modeling and coding. IEEE Transactions on Information Theory 27
1981
Earlier work this paper cites.
LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Advances in neural information processing systems. pp. 598–605 (1990)
1990
Earlier work this paper cites.
Reed, R.: Pruning algorithms-a survey. IEEE transactions on Neural Networks 4
1993
Earlier work this paper cites.
Chellapilla, K., Puri, S., Simard, P.: High performance convolutional neural networks for document processing. In: Tenth international workshop on frontiers in handwriting recognition. Suvisoft (2006)
2006
Earlier work this paper cites.
Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68
2006
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Chen, W., Wilson, J., Tyree, S., Weinberger, K., Chen, Y.: Compressing neural networks with the hashing trick. In: International conference on machine learning. pp. 2285–2294. PMLR (2015)
2015
Earlier work this paper cites.
Courbariaux, M., Bengio, Y., David, J.P.: Binaryconnect: Training deep neural networks with binary weights during propagations. In: Advances in neural information processing systems. pp. 3123–3131 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. pp. 1026–1034 (2015)
2015
Earlier work this paper cites.
Chen, W., Wilson, J., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing convolutional neural networks in the frequency domain. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1475–1484 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Li, F., Zhang, B., Liu, B.: Ternary weight networks. arXiv preprint arXiv:1605.04711 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: European conference on computer vision. pp. 525–542. Springer (2016)
2016
Earlier work this paper cites.
Wang, Y., Xu, C., You, S., Tao, D., Xu, C.: Cnnpack: Packing convolutional neural networks in the frequency domain. In: NIPS. vol. 1, p. 3 (2016)
2016
Earlier work this paper cites.
Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4820–4828 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
He, Y., Zhang, X., Sun, J.: Channel pruning for accelerating very deep neural networks. In: Proceedings of the IEEE international conference on computer vision. pp. 1389–1397 (2017)
2017
Earlier work this paper cites.
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Quantized neural networks: Training neural networks with low precision weights and activations. The Journal of Machine Learning Research 18
2017
Earlier work this paper cites.
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2736–2744 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Luo, J.H., Wu, J., Lin, W.: Thinet: A filter level pruning method for deep neural network compression. In: Proceedings of the IEEE international conference on computer vision. pp. 5058–5066 (2017)
2017
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
Wang, K., Liu, Z., Lin, Y., Lin, J., Han, S.: Haq: Hardware-aware automated quantization with mixed precision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8612–8620 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Choi, Y., et al.: Compression of deep cnns under joint sparsity constraints. arXiv:1805.08303 (2018)
2018
Cited alongside, same era.
Dubey, A., Chatterjee, M., Ahuja, N.: Coreset-based neural network compression. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 454–470 (2018)
2018
Cited alongside, same era.
Faraone, J., Fraser, N., Blott, M., Leong, P.H.: Syq: Learning symmetric quantization for efficient deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4300–4309 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
Zhou, Y., Zhang, Y., Wang, Y., Tian, Q.: Accelerate cnn via recursive bayesian pruning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3306–3315 (2019)
2019
Later among the works it cites.
Brix, C., Bahar, P., Ney, H.: Successfully applying the stabilized lottery ticket hypothesis to the transformer architecture. ACL (2020). https://doi.org/10.18653/v1/2020.acl-main.360, https://www.aclweb.org/anthology/2020.acl-main.360
2020
Later among the works it cites.
Chen, T., Frankle, J., Chang, S., Liu, S., Zhang, Y., Wang, Z., Carbin, M.: The lottery ticket hypothesis for pre-trained bert networks. In: NeurIPS (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Frankle, J., Dziugaite, G.K., Roy, D., Carbin, M.: Linear mode connectivity and the lottery ticket hypothesis. In: International Conference on Machine Learning. pp. 3259–3269. PMLR (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Liu, T., Zenke, F.: Finding trainable sparse networks through neural tangent transfer. In: International Conference on Machine Learning. pp. 6336–6347. PMLR (2020)
2020
Later among the works it cites.
Malach, E., Yehudai, G., Shalev-Schwartz, S., Shamir, O.: Proving the lottery ticket hypothesis: Pruning is all you need. In: International Conference on Machine Learning. pp. 6682–6691. PMLR (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Tu, C.H., Lee, J.H., Chan, Y.M., Chen, C.S.: Pruning depthwise separable convolutions for mobilenet compression. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
You, H., Li, C., Xu, P., Fu, Y., Wang, Y., Chen, X., Baraniuk, R.G., Wang, Z., Lin, Y.: Drawing early-bird tickets: Toward more efficient training of deep networks. In: ICLR (2020), https://openreview.net/forum?id=BJxsrgStvr
2020
Later among the works it cites.
Yu, H., S, S.E., Y, Y.T., Morcos, A.S.: Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP. In: ICLR (2020), https://openreview.net/forum?id=S1xnXRVFwH
2020
Later among the works it cites.
2021
Later among the works it cites.
Girish, S., Maiya, S.R., Gupta, K., Chen, H., Davis, L.S., Shrivastava, A.: The lottery ticket hypothesis for object recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 762–771 (2021)
2021
Later among the works it cites.
Yeom, S.K., Seegerer, P., Lapuschkin, S., Binder, A., Wiedemann, S., Müller, K.R., Samek, W.: Pruning by explaining: A novel criterion for deep neural network pruning. Pattern Recognition 115
2021
Later among the works it cites.
Young, S., Wang, Z., Taubman, D., Girod, B.: Transform quantization for cnn compression. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
2021
Later among the works it cites.
Leclerc, G., Ilyas, A., Engstrom, L., Park, S.M., Salman, H., Madry, A.: ffcv. https://github.com/libffcv/ffcv/ (2022)
2022
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