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Symbolic equations are at the core of scientific discovery.
Practical Methods of Optimization
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Genetic programming as a means for programming computers by natural selection
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 2004
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Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
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Variational learning of inducing variables in sparse gaussian processes
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Eureqa: software review, 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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The cma evolution strategy: A tutorial
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Extrapolation and learning equations
Martius, G. and Lampert, C. H · 2016
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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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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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The bitter lesson
Sutton, R · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Discovering symbolic models from deep learning with inductive biases
Cranmer, M., Sanchez-Gonzalez, A., Battaglia, P., Xu, R., Cranmer, K., Spergel, D., and Ho, S · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N · 2020
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Learning equations for extrapolation and control
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Demystifying black-box models with symbolic metamodels
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Deep learning for symbolic mathematics
Lample, G. and Charton, F · 2019
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Analysing mathematical reasoning abilities of neural models
Saxton, D., Grefenstette, E., Hill, F., and Kohli, P · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G
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Generative language modeling for automated theorem proving, 2020
Polu, S. and Sutskever, I · 2020
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Ai feynman: A physics-inspired method for symbolic regression
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Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Udrescu, S.-M., Tan, A., Feng, J., Neto, O., Wu, T., and Tegmark, M · 2020
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Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Petersen, B. K · 2021
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Learning transferable visual models from natural language supervision, 2021
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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