Abstract
Long Short-Term Memory (LSTM) is a powerful recurrent neural network architecture that is successfully used in many sequence modeling applications. Inside an LSTM unit, a vector called “memory cell” is used to memorize the history. Another important vector, which works along with the memory cell, represents hidden states and is used to make a prediction at a specific step. Memory cells record the entire history, while the hidden states at a specific time step in general need to attend only to very limited information thereof. Therefore, there exists an imbalance between the huge information carried by a memory cell and the small amount of information requested by the hidden states at a specific step. We propose to explicitly impose sparsity on the hidden states to adapt them to the required information. Extensive experiments show that sparsity reduces the computational complexity and improves the performance of LSTM networks (The source code is available at https://github.com/feiyuhug/SHS_LSTM/tree/master).
Keywords
We acknowledge support by NSFC (61621136008) and German Research Foundation (DFG) under project CML (TRR 169).
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Anderson, P., et al.: Bottom-up and top-down attention for image captioning and visual question answering. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018). https://doi.org/10.1109/cvpr.2018.00636
Barlow, H.: What is the computational goal of the neocortex? In: Koch, C., Davis, J. (eds.) Large-Scale Neuronal Theories of the Brain, pp. 1–22. The MIT Press, Cambridge (1994)
Campos, V., Jou, B., Giró-i Nieto, X., Torres, J., Chang, S.F.: Skip RNN: learning to skip state updates in recurrent neural networks. arXiv preprint arXiv:1708.06834 (2017)
Chalk, M., Marre, O., Tkačik, G.: Toward a unified theory of efficient, predictive, and sparse coding. Proc. Natl. Acad. Sci. 115(1), 186–191 (2018). https://doi.org/10.1101/152660
Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111 (2014). https://doi.org/10.3115/v1/w14-4012
Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013). https://doi.org/10.1109/icassp.2013.6638947
Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems, pp. 1135–1143 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.4324/9781315174105-4
Jernite, Y., Grave, E., Joulin, A., Mikolov, T.: Variable computation in recurrent neural networks. arXiv preprint arXiv:1611.06188 (2016)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3128–3137 (2015). https://doi.org/10.1109/cvpr.2015.7298932
Lin, J., Rao, Y., Lu, J., Zhou, J.: Runtime neural pruning. In: Advances in Neural Information Processing Systems, pp. 2178–2188 (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: the penn treebank. Comput. Linguist. 19(2), 313–330 (1993)
McGill, M., Perona, P.: Deciding how to decide: dynamic routing in artificial neural networks. arXiv preprint arXiv:1703.06217 (2017)
Mikolov, T., Karafiát, M., Burget, L., Černockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Eleventh Annual Conference of the International Speech Communication Association (2010)
Narang, S., Elsen, E., Diamos, G., Sengupta, S.: Exploring sparsity in recurrent neural networks. arXiv preprint arXiv:1704.05119 (2017)
Olshausen, B.A., Field, D.J.: Sparse coding of sensory inputs. Curr. Opinion Neurobiol. 14(4), 481–487 (2004). https://doi.org/10.1016/j.conb.2004.07.007
Rennie, S.J., Marcheret, E., Mroueh, Y., Ross, J., Goel, V.: Self-critical sequence training for image captioning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7008–7024 (2017). https://doi.org/10.1109/cvpr.2017.131
Shazeer, N., et al.: Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538 (2017)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3156–3164. IEEE (2015). https://doi.org/10.1109/cvpr.2015.7298935
Wen, W., et al.: Learning intrinsic sparse structures within long short-term memory. arXiv preprint arXiv:1709.05027 (2017)
Yu, N., Qiu, S., Hu, X., Li, J.: Accelerating convolutional neural networks by group-wise 2D-filter pruning. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2502–2509. IEEE (2017). https://doi.org/10.1109/ijcnn.2017.7966160
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)
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Yu, N., Weber, C., Hu, X. (2019). Learning Sparse Hidden States in Long Short-Term Memory. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_24
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