Fetching the paper…
Reading the bibliography…
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set.
1902
Earlier work this paper cites.
1907
Earlier work this paper cites.
1911
Earlier work this paper cites.
J. Koza, “Genetic programming as a means for programming computers by natural selection,” Statistics and Computing , vol. 4, no. 2, pp. 87–112, jun 1994. [Online]. Available: http://link.springer.com/10.1007/BF00175355
1994
Earlier work this paper cites.
B. K. Natarajan, “Sparse Approximate Solutions to Linear Systems,” SIAM Journal on Computing , vol. 24, no. 2, pp. 227–234, apr 1995. [Online]. Available: http://epubs.siam.org/doi/10.1137/S0097539792240406
1995
Earlier work this paper cites.
M. Schmidt and H. Lipson, “Distilling free-form natural laws from experimental data.” Science (New York, N.Y.) , vol. 324, no. 5923, pp. 81–5, apr 2009. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/19342586
2009
Earlier work this paper cites.
Z. Xu, H. Zhang, Y. Wang, X. Chang, and Y. Liang, “L 1/2 regularization,” Science China Information Sciences , vol. 53, no. 6, pp. 1159–1169, jun 2010. [Online]. Available: http://link.springer.com/10.1007/s11432-010-0090-0
2010
Earlier work this paper cites.
Z.-B. Xu, H.-L. Guo, Y. Wang, and H. Zhang, “Representative of L1/2 Regularization among Lq (0 < q ≤ \leq 1) Regularizations: an Experimental Study Based on Phase Diagram,” Acta Automatica Sinica , vol. 38, no. 7, pp. 1225–1228, jul 2012. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1874102911602930
2012
Earlier work this paper cites.
T. Tieleman and G. Hinton, “Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude,” COURSERA: Neural networks for machine learning , vol. 4, no. 2, pp. 26–31, 2012
2012
Earlier work this paper cites.
Q. Fan, J. M. Zurada, and W. Wu, “Convergence of online gradient method for feedforward neural networks with smoothing L1/2 regularization penalty,” Neurocomputing , vol. 131, pp. 208–216, may 2014. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231213010825
2014
Earlier work this paper cites.
W. Wu, Q. Fan, J. M. Zurada, J. Wang, D. Yang, and Y. Liu, “Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks,” Neural Networks , vol. 50, pp. 72–78, feb 2014. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0893608013002700
2014
Cited alongside, same era.
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: http://tensorflow.org/
2015
Cited alongside, same era.
A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun, “The loss surfaces of multilayer networks,” in Journal of Machine Learning Research , vol. 38. Microtome Publishing, 2015, pp. 192–204
2017
Later among the works it cites.
S. Srinivas, A. Subramanya, and R. V. Babu, “Training Sparse Neural Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . IEEE, jul 2017, pp. 455–462. [Online]. Available: http://ieeexplore.ieee.org/document/8014795/
2017
Later among the works it cites.
2018
Later among the works it cites.
Z. Long, Y. Lu, X. Ma, and B. Dong, “PDE-Net: Learning PDEs from Data,” in Proceedings of Machine Learning Research , jul 2018, pp. 3208–3216. [Online]. Available: http://proceedings.mlr.press/v80/long18a.html
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Discovering governing equations from data by sparse identification of nonlinear dynamical systems.” Proceedings of the National Academy of Sciences of the United States of America , vol. 113, no. 15, pp. 3932–7, apr 2016. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/27035946http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4839439
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Data-driven discovery of partial differential equations,” Science Advances , vol. 3, no. 4, p. e1602614, apr 2017
2017
Cited alongside, same era.
H. Schaeffer, “Learning partial differential equations via data discovery and sparse optimization,” Proceedings of the Royal Society A , vol. 473, no. 2197, 2017. [Online]. Available: http://rspa.royalsocietypublishing.org/content/royprsa/473/2197/20160446.full.pdf
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Molchanov, A. Ashukha, and D. Vetrov, “Variational dropout sparsifies deep neural networks,” in 34th International Conference on Machine Learning, ICML 2017 , vol. 5. International Machine Learning Society (IMLS), 2017, pp. 3854–3863
2017
Cited alongside, same era.
2018
Later among the works it cites.
A. Trask, F. Hill, S. Reed, J. Rae, C. Dyer, and P. Blunsom, “Neural Arithmetic Logic Units,” aug 2018
2018
Later among the works it cites.
D. Zheng, V. Luo, J. Wu, and J. B. Tenenbaum, “Unsupervised learning of latent physical properties using perception-prediction networks,” in 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 , vol. 1. Association For Uncertainty in Artificial Intelligence (AUAI), 2018, pp. 497–507
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Rudy, A. Alla, S. L. Brunton, and J. N. Kutz, “Data-driven identification of parametric partial differential equations,” SIAM Journal on Applied Dynamical Systems , vol. 18, no. 2, pp. 643–660, apr 2019
2019
Closest in time.