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Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints.
Problems of organic growth
von Bertalanffy, L · 1949
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A new approach to linear filtering and prediction problems
Kalman, R. E · 1960
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A useful method for model-building
Box, G. E. P. and Hunter, W. G · 1962
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Graphical representation and stability conditions of predator-prey interaction
Rosenzweig, M. and MacArthur, R · 1963
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Statistical inference for probabilistic functions of finite state markov chains
Baum, L. E. and Petrie, T · 1966
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A tutorial on hidden markov models and selected applications in speech recognition
Rabiner, L · 1989
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Inference from iterative simulation using multiple sequences
Gelman, A. and Rubin, D. B · 1992
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Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series
McKinney, B. A., Crowe, J. E., Voss, H. U., Crooke, P. S., Barney, N., and Moore, J. H · 2006
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Automated reverse engineering of nonlinear dynamical systems
Bongard, J. C. and Lipson, H · 2007
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Church: a language for generative models
Goodman, N. D., Mansinghka, V. K., Roy, D. M., Bonawitz, K., and Tenenbaum, J. B · 2008
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Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
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Structure discovery in nonparametric regression through compositional kernel search
Duvenaud, D., Lloyd, J., Grosse, R., Tenenbaum, J., and Zoubin, G · 2013
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Bayesian data analysis, third edition
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B · 2013
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Gaussian process kernels for pattern discovery and extrapolation
Wilson, A. G. and Adams, R. P · 2013
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Build, compute, critique, repeat: Data analysis with latent variable models
Blei, D. M · 2014
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Model selection in compositional spaces
Grosse, R. B · 2014
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
Homan, M. D. and Gelman, A · 2014
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Automatic construction and natural-language description of nonparametric regression models
Lloyd, J. R., Duvenaud, D. K., Grosse, R. B., Tenenbaum, J. B., and Ghahramani, Z · 2014
Cited alongside, same era.
A new approach to probabilistic programming inference
Wood, F., van de Meent, J. W., and Mansinghka, V · 2014
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Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Vehtari, A., Gelman, A., and Gabry, J · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Cited alongside, same era.
Neural ordinary differential equations
PyMC: a modern, and comprehensive probabilistic programming framework in python
Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., and Zinkov, R · 2023
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GPT-4 Technical Report
Achiam, O. J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., Avila, R., Babuschkin, I., Balaji, S., Balcom, V., Baltescu, P., Bao, H., Bavarian, M., Belgum, J., Bello, I., Berdine, J., Bernadett-Shapiro, G., Berner, C., Bogdonoff, L., Boiko, O., Boyd, M., Brakman, A.-L., Brockman, G., Brooks, T., Brundage, M., Button, K., Cai, T., Campbell, R., Cann, A., Carey, B., Carlson, C., Carmichael, R., Chan, B., Chang, C., Chantzis, F., Chen, D., Chen, S., Chen, R., Chen, J., Chen, M., Chess, B., Cho, C., Chu, C., Chung, H. W., Cummings, D., Currier, J., Dai, Y., Decareaux, C., Degry, T., Deutsch, N., Deville, D., Dhar, A., Dohan, D., Dowling, S., Dunning, S., Ecoffet, A., Eleti, A., Eloundou, T., Farhi, D., Fedus, L., Felix, N., Fishman, S. P., Forte, J., Fulford, I., Gao, L., Georges, E., Gibson, C., Goel, V., Gogineni, T., Goh, G., Gontijo-Lopes, R., Gordon, J., Grafstein, M., Gray, S., Greene, R., Gross, J., Gu, S. S., Guo, Y., Hallacy, C., Han, J., Harris, J., He, Y., Heaton, M., Heidecke, J., Hesse, C., Hickey, A., Hickey, W., Hoeschele, P., Houghton, B., Hsu, K., Hu, S., Hu, X., Huizinga, J., Jain, S., Jain, S., Jang, J., Jiang, A., Jiang, R., Jin, H., Jin, D., Jomoto, S., Jonn, B., Jun, H., Kaftan, T., Kaiser, L., Kamali, A., Kanitscheider, I., Keskar, N. S., Khan, T., Kilpatrick, L., Kim, J. W., Kim, C., Kim, Y., Kirchner, H., Kiros, J. R., Knight, M., Kokotajlo, D., Kondraciuk, L., Kondrich, A., Konstantinidis, A., Kosic, K., Krueger, G., Kuo, V., Lampe, M., Lan, I., Lee, T., Leike, J., Leung, J., Levy, D., Li, C. M., Lim, R., Lin, M., Lin, S., Litwin, M., Lopez, T., Lowe, R., Lue, P., Makanju, A. A., Malfacini, K., Manning, S., Markov, T., Markovski, Y., Martin, B., Mayer, K., Mayne, A., McGrew, B., McKinney, S. M., McLeavey, C., McMillan, P., McNeil, J., Medina, D., Mehta, A., Menick, J., Metz, L., Mishchenko, A., Mishkin, P., Monaco, V., Morikawa, E., Mossing, D. P., Mu, T., Murati, M., Murk, O., M’ely, D., Nair, A., Nakano, R., Nayak, R., Neelakantan, A., Ngo, R., Noh, H., Long, O., O’Keefe, C., Pachocki, J. W., Paino, A., Palermo, J., Pantuliano, A., Parascandolo, G., Parish, J., Parparita, E., Passos, A., Pavlov, M., Peng, A., Perelman, A., de Avila Belbute Peres, F., Petrov, M., de Oliveira Pinto, H. P., Pokorny, M., Pokrass, M., Pong, V. H., Powell, T., Power, A., Power, B., Proehl, E., Puri, R., Radford, A., Rae, J., Ramesh, A., Raymond, C., Real, F., Rimbach, K., Ross, C., Rotsted, B., Roussez, H., Ryder, N., Saltarelli, M. D., Sanders, T., Santurkar, S., Sastry, G., Schmidt, H., Schnurr, D., Schulman, J., Selsam, D., Sheppard, K., Sherbakov, T., Shieh, J., Shoker, S., Shyam, P., Sidor, S., Sigler, E., Simens, M., Sitkin, J., Slama, K., Sohl, I., Sokolowsky, B. D., Song, Y., Staudacher, N., Such, F. P., Summers, N., Sutskever, I., Tang, J., Tezak, N. A., Thompson, M., Tillet, P., Tootoonchian, A., Tseng, E., Tuggle, P., Turley, N., Tworek, J., Uribe, J. F. C., Vallone, A., Vijayvergiya, A., Voss, C., Wainwright, C., Wang, J. J., Wang, A., Wang, B., Ward, J., Wei, J., Weinmann, C., Welihinda, A., Welinder, P., Weng, J., Weng, L., Wiethoff, M., Willner, D., Winter, C., Wolrich, S., Wong, H., Workman, L., Wu, S., Wu, J., Wu, M., Xiao, K., Xu, T., Yoo, S., Yu, K., Yuan, Q., Zaremba, W., Zellers, R., Zhang, C., Zhang, M., Zhao, S., Zheng, T., Zhuang, J., Zhuk, W., and Zoph, B · 2023
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Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
Cited alongside, same era.
N-beats: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y · 2019
Cited alongside, same era.
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., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Cited alongside, same era.
Learning insulin-glucose dynamics in the wild
Miller, A. C., Foti, N. J., and Fox, E · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Ponde, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D. W., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Babuschkin, I., Balaji, S., Jain, S., Carr, A., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M. M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Cited alongside, same era.
On Neural Differential Equations
Kidger, P · 2021
Cited alongside, same era.
An introduction to probabilistic programming, 2021
van de Meent, J.-W., Paige, B., Yang, H., and Wood, F · 2021
Cited alongside, same era.
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PAL: program-aided language models
Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., and Neubig, G · 2023
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Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Lee, P., Bubeck, S., and Petro, J · 2023
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Automated model discovery for human brain using constitutive artificial neural networks
Linka, K., St. Pierre, S. R., and Kuhl, E · 2023
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posteriordb: a set of posteriors for Bayesian inference and probabilistic programming, October 2023
Magnusson, M., Bürkner, P., and Vehtari, A · 2023
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Code Llama: Open Foundation Models for Code
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X., Adi, Y., Liu, J., Remez, T., Rapin, J., Kozhevnikov, A., Evtimov, I., Bitton, J., Bhatt, M. P., Ferrer, C. C., Grattafiori, A., Xiong, W., D’efossez, A., Copet, J., Azhar, F., Touvron, H., Martin, L., Usunier, N., Scialom, T., and Synnaeve, G · 2023
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Sequential monte carlo learning for time series structure discovery
Saad, F. A., Patton, B., Hoffman, M., Saurous, R. A., and Mansinghka, V. K · 2023
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
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Reflexion: language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K. R., and Yao, S · 2023
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Wong, L. S., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., and Tenenbaum, J. B · 2023
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An Empirical Study on Challenging Math Problem Solving with GPT-4, 2023
Wu, Y., Jia, F., Zhang, S., Li, H., Zhu, E., Wang, Y., Lee, Y. T., Peng, R., Wu, Q., and Wang, C · 2023
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Goal driven discovery of distributional differences via language descriptions
Zhong, R., Zhang, P., Li, S., Ahn, J., Klein, D., and Steinhardt, J · 2023
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Phenomenal yet puzzling: Testing inductive reasoning capabilities of language models with hypothesis refinement
Qiu, L., Jiang, L., Lu, X., Sclar, M., Pyatkin, V., Bhagavatula, C., Wang, B., Kim, Y., Choi, Y., Dziri, N., and Ren, X · 2024
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Hypothesis search: Inductive reasoning with language models
Wang, R., Zelikman, E., Poesia, G., Pu, Y., Haber, N., and Goodman, N. D · 2024
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