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Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points.
Studies in molecular dynamics. i. general method
Alder, B. J. and Wainwright, T. E · 1959
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Genetic programming: on the programming of computers by means of natural selection , volume 1
Koza, J. R. and Koza, J. R · 1992
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Learning from hints
Abu-Mostafa, Y. S · 1994
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Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
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Multi-armed bandits with episode context
Rosin, C. D · 2011
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DEAP: Evolutionary algorithms made easy
Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A., Parizeau, M., and Gagné, C · 2012
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Syntax-guided synthesis
Alur, R., Bodík, R., Juniwal, G., Martin, M. M. K., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E., and Udupa, A · 2013
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A symbolic regression-based modelling strategy of ac/dc rectifiers for rfid applications
Ceperic, V., Bako, N., and Baric, A · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Explaining unemployment rates with symbolic regression
Truscott, P. and Korns, M. F · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X · 2016
Cited alongside, same era.
Deepcoder: Learning to write programs
Balog, M., Gaunt, A. L., Brockschmidt, M., Nowozin, S., and Tarlow, D · 2016
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Quantification of survey expectations by means of symbolic regression via genetic programming to estimate economic growth in central and eastern european economies
Claveria, O., Monte, E., and Torra, S · 2016
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Coarse-grained protein models and their applications
Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E., and Kolinski, A · 2016
Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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Sympy: symbolic computing in python
Meurer, A., Smith, C. P., Paprocki, M., Čertík, O., Kirpichev, S. B., Rocklin, M., Kumar, A., Ivanov, S., Moore, J. K., Singh, S., Rathnayake, T., Vig, S., Granger, B. E., Muller, R. P., Bonazzi, F., Gupta, H., Vats, S., Johansson, F., Pedregosa, F., Curry, M. J., Terrel, A. R., Roučka, v., Saboo, A., Fernando, I., Kulal, S., Cimrman, R., and Scopatz, A · 2017
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Neuro-symbolic program synthesis
Parisotto, E., Mohamed, A., Singh, R., Li, L., Zhou, D., and Kohli, P · 2017
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Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
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Leveraging grammar and reinforcement learning for neural program synthesis
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Cited alongside, same era.
Prediction of dynamical systems by symbolic regression
Quade, M., Abel, M., Shafi, K., Niven, R. K., and Noack, B. R · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
Cited alongside, same era.
Robustfill: Neural program learning under noisy I/O
Devlin, J., Uesato, J., Bhupatiraju, S., Singh, R., Mohamed, A., and Kohli, P · 2017
Cited alongside, same era.
Program synthesis
Gulwani, S., Polozov, O., and Singh, R · 2017
Cited alongside, same era.
Symbolic regression for the estimation of transfer functions of hydrological models
Klotz, D., Herrnegger, M., and Schulz, K · 2017
Cited alongside, same era.
Bunel, R., Hausknecht, M. J., Devlin, J., Singh, R., and Kohli, P · 2018
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Sisso: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., and Ghiringhelli, L. M · 2018
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Learning equations for extrapolation and control
Sahoo, S. S., Lampert, C. H., and Martius, G · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Segler, M. H., Preuss, M., and Waller, M. P · 2018
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Learning a meta-solver for syntax-guided program synthesis
Si, X., Yang, Y., Dai, H., Naik, M., and Song, L · 2018
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Neural-guided deductive search for real-time program synthesis from examples
Vijayakumar, A. J., Mohta, A., Polozov, O., Batra, D., Jain, P., and Gulwani, S · 2018
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