MIT Press, Cambridge (2016)īattaglia, P.W., et al.: Relational inductive biases, deep learning, and graph networks. ![]() Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. In: International Conference on Learning Representations (2021)īishop, C.M.: Pattern Recognition and Machine Learning. Petersen, B.K., Larma, M.L., Mundhenk, T.N., Santiago, C.P, Kim, S.K., Kim, J.T.: Deep symbolic regression: recovering mathematical expressions from data via risk-seeking policy gradients. Other articles where regression is discussed: defense mechanism: Regression is a return to. It provides the values of the dependent variable from the. Udrescu, S.M., Tegmark, M.: AI Feynman: a physics-inspired method for symbolic regression. Regression analysis is a statistical technique of measuring the relationship between variables. Halzen, F., Martin, A.D.: Quarks and Leptons: An Introductory Course in Modern Particle Physics. In: NeurIPS 2021 Datasets and Benchmarks Track (Round 1) (2021) La Cava, W., et al.: Contemporary symbolic regression methods and their relative performance. If science was unprepared for the influx of careerists, it was even less prepared for the blossoming. The philosopher Gilbert Ryle (1949) was concerned with critiquing what he called the intellectualist legend, which required intelligent acts to be the product of the conscious application of mental rules. ![]() arXiv:2006.11287 Ĭhampion, K., Lusch, B., Kutz, J.N., Brunton, S.L.: Data-driven discovery of coordinates and governing equations. Ryles Regress is a classic argument against cognitivist theories, and concludes that such theories cannot be scientific. The problem of infinite regress came up in the context of his idea that material events are dependent on priors, which is how I came across this blog via Google. Cranmer, M., et al.: Discovering symbolic models from deep learning with inductive biases. Douglas has been spending his retirement years visiting college campuses and promoting his 13- (or more) step proof for the existence of God.
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