ML and realism references to use

  1. Alai_2004_AI_Scientific_Discovery_and_Realism1
  2. Aurisano_2016_A_convolutional_neural_network_neutrino_event2
  3. Bengio_2009_Learning_Deep_Architectures_for_AI3
  4. Bensusan_2000_Is_machine_learning_experimental_philosophy4
  5. Bueno_2014_Computer_Simulations_An_Inferential_Conception5
  6. Button_2016_Structure_and_Categoricity_Determinacy6
  7. Carnap_1945_On_Inductive_Logic7
  8. Carnap_1945_Two_Concepts_of_Probability8
  9. Carnap_1947_On_the_Application_of_Inductive_Logic9
  10. Carnap_1947_Probability_as_a_Guide_in_Life10
  11. Carnap_1950_Empiricism_Semantics_and_Ontology11
  12. Copeland_1996_On_Alan_Turings_anticipation_of_connectionism12
  13. Cowan_1998_Statistical_Data_Analysis13
  14. Cowan_2011_Asymptotic_formulae_for_likelihood_based_tests14
  15. Cowan_2016_Statistics15
  16. Cranmer_2015_Practical_Statistics_for_the_LHC16
  17. David_2015_Deepsign_Deep_learning_for_automatic_malware17
  18. Davies_1987_Hypothesis_testing_when_a_nuisance_parameter_is18
  19. DiNardo_2009_Introductory_remarks_on_metastatistics19
  20. Farley_1954_Simulation_of_Self_Organizing_Systems_by_Digital20
  21. Freund_1996_Experiments_with_a_new_boosting_algorithm21
  22. Fukushima_1980_Neocognitron_A_self_organizing_neural_network22
  23. Ghahramani_2015_Probabilistic_machine_learning_and_artificial23
  24. Good_1988_The_interface_between_statistics_and_philosophy24
  25. Goodell_2016_The_Rise_of_Intellegent_Machines25
  26. Goodman_1955_Fact_Fiction_and_Forecast26
  27. Guest_2016_Jet_flavor_classification_in_high_energy_physics27
  28. Hacking_2001_An_Introduction_to_Probability_and_Inductive_Logic28
  29. Hastie_2009_The_Elements_of_Statistical_Learning_Data_Mining29
  30. Hennig_2015_What_are_the_true_clusters30
  31. Huang_2016_Unsupervised_learning_of_discriminative_attributes31
  32. Hume_2007_An_Enquiry_Concerning_Human_Understanding32
  33. James_2006_Statistical_Methods_in_Experimental_Particle33
  34. Kendall_1946_The_Advanced_Theory_of_Statistics_VolII34
  35. Korb_2004_Introduction_Machine_learning_as_philosophy35
  36. Krizhevsky_2012_ImageNet_Classification_with_Deep_Convolutional36
  37. Le_2013_Building_high_level_features_using_large_scale37
  38. LeCun_1989_Backpropagation_applied_to_handwritten_zip_code38
  39. LeCun_1998_Gradient_based_learning_applied_to_document39
  40. LeCun_2015_Deep_learning40
  41. Louppe_2017_QCD_Aware_Recursive_Neural_Networks_for_Jet41
  42. MacFarlane_2017_Rudolf_Carnap_1891_197042
  43. Mayo_1996_Error_and_the_Growth_of_Experimental_Knowledge43
  44. Mcculloch_1943_A_logical_calculus_of_the_ideas_immanent44
  45. Miller_2014_Realism45
  46. Plaut_2018_From_Principal_Subspaces_to_Principal_Components46
  47. Psillos_1999_Scientific_Realism_How_Science_Tracks_Truth47
  48. Psillos_2016_The_Realist_Turn_in_the_Philosophy_of_Science48
  49. Quine_1969_Natural_kinds49
  50. Reichenbach_1938_Experience_and_Prediction50
  51. Reichenbach_1940_On_the_Justification_of_Induction51
  52. Rochester_1956_Tests_on_a_cell_assembly_theory_of_the_action52
  53. Rosenblatt_1958_The_Perceptron_A_Probabilistic_Model53
  54. Salmon_1963_On_Vindicating_Induction54
  55. Salmon_1966_The_Foundations_of_Scientific_Inference55
  56. Salmon_1991_Hans_Reichenbachs_Vindication_of_Induction56
  57. Schmidhuber_2015_Deep_learning_in_neural_networks_An_overview57
  58. Sellars_1964_Induction_as_Vindication58
  59. Sider_2011_Writing_the_Book_of_the_World59
  60. Solomonoff_1996_Does_Algorithmic_Probability_Solve_the_Problem60
  61. Solomonoff_1997_The_Discovery_of_Algorithmic_Probability61
  62. Theodoridis_2009_Pattern_Recognition62
  63. Turing_2004_Intelligent_Machinery63
  64. Wald_1943_Tests_of_Statistical_Hypotheses_Concerning_Several64
  65. Wang_2017_On_the_Origin_of_Deep_Learning65
  66. Weintraub_1995_What_was_Humes_Contribution_to_the_Problem66
  67. Wilks_1938_Large_sample_distribution_of_the_likelihood_ratio67
  68. Williamson_2004_A_dynamic_interaction_between_machine_learning68
  69. Williamson_2009_Probabilistic_theories_of_causality69
  70. Williamson_2010_The_Philosophy_of_Science_and_its_relation70
  71. Williamson_2011_Objective_Bayesianism_Bayesian_conditionalisation71
  72. vanFraassen_1980_The_Scientific_Image72

References

Alai, M. (2004). AI, Scientific Discovery and Realism. Minds and Machines, 14, 21–42.
Aurisano, A. et al. (2016). A convolutional neural network neutrino event classifier. Journal of Instrumentation, 11, P09001. https://arxiv.org/abs/1604.01444
Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, 2, 1–127.
Bensusan, H. (2000). Is machine learning experimental philosophy of science? In A. Aliseda & D. Pearce (Eds.), ECAI2000 Workshop notes on scientific Reasoning in Artificial Intelligence and the Philosophy of Science (pp. 9–14).
Bueno, O. (2014). Computer Simulations: An Inferential Conception. The Monist, 97, 378–398.
Button, T. & Walsh, S. (2016). Structure and Categoricity: Determinacy of Reference and Truth-Value in the Philosophy of Mathematics. https://arxiv.org/abs/1501.00472
Carnap, R. (1945a). On Inductive Logic. Philosophy of Science, 12, 72–97.
———. (1945b). Two Concepts of Probability. The Journal of Philosophy, 5, 513–532.
———. (1947a). On the Application of Inductive Logic. Philosophy and Phenomenological Research, 8, 133–148.
———. (1947b). Probability as a Guide in Life. The Journal of Philosophy, 44, 141–148.
———. (1950). Empiricism, Semantics, and Ontology. Revue Internationale de Philosophie, 4, 20–40.
Copeland, B. J. & Proudfoot, D. (1996). On Alan Turing’s anticipation of connectionism. Synthese, 108, 361–377.
Cowan, G. (1998). Statistical Data Analysis. Oxford: Clarendon Press.
———. (2016). Statistics. In C. Patrignani et al. (Particle Data Group), Chinese Physics C, 40, 100001. http://pdg.lbl.gov/2016/reviews/rpp2016-rev-statistics.pdf.
Cowan, G., Cranmer, K., Gross, E., & Vitells, O. (2011). Asymptotic formulae for likelihood-based tests of new physics. European Physical Journal C, 71, 1544. https://arxiv.org/abs/1007.1727
Cranmer, K. (2015). Practical Statistics for the LHC. https://arxiv.org/abs/1503.07622
David, O. E. & Netanyahu, N. S. (2015). Deepsign: Deep learning for automatic malware signature generation and classification. In IJCNN 2015: International Joint Conference on Neural Networks (pp. 1–8). IEEE. http://ieeexplore.ieee.org/document/7280815/
Davies, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternatives. Biometrika, 74, 33–43.
DiNardo, J. (2009). Introductory remarks on metastatistics for the practically minded non-Bayesian regression runner. In Palgrave Handbook of Econometrics (pp. 98–165). Palgrave Macmillan UK.
Farley, B. G. & Clark, W. A. (1954). Simulation of Self-Organizing Systems by Digital Computer. IRE Transactions on Information Theory, 4, 76–84.
Freund, Y. & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning. (Vol. 96, pp. 148-156).
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202.
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521, 452–459.
Good, I. J. (1988). The interface between statistics and philosophy of science. Statistical Science, 3, 386–397.
Goodell, J. (2016). The Rise of Intellegent Machines. Rolling Stone. May 4, 2016. http://rollingstoneaus.com/culture/post/the-rise-of-intelligent-machines-part-1/3751
Goodman, N. (1955). Fact, Fiction, and Forecast. Cambridge, MA: Harvard University Press.
Guest, D. et al. (2016). Jet flavor classification in high-energy physics with deep neural networks. Physical Review D, 94, 112002. https://arxiv.org/abs/1607.08633
Hacking, I. (2001). An Introduction to Probability and Inductive Logic. Cambridge: Cambridge University Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Hennig, C. (2015). What are the true clusters? Pattern Recognition Letters, 64, 53–62. https://arxiv.org/abs/1502.02555
Huang, C., Loy, C. C., & Tang, X. (2016). Unsupervised learning of discriminative attributes and visual representations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5175–5184).
Hume, D. (2007). An Enquiry Concerning Human Understanding. (P. Millican, Ed.). Oxford: Oxford University Press. (Originally published in 1748).
James, F. (2006). Statistical Methods in Experimental Particle Physics. World Scientific.
Kendall, M. G. (1946). The Advanced Theory of Statistics, Vol.II. London: Charles Griffin & Company.
Korb, K. B. (2004). Introduction: Machine learning as philosophy of science. Minds and Machines, 14, 433–440.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In L. B. F. Pereira C. J. C. Burges & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25 (pp. 1097–1105). Curran Associates. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
Le, Q. V. and et al. (2013). Building high-level features using large scale unsupervised learning. In Acoustics, Speech and Signal Processing (ICASSP) (pp. 8595–8598). IEEE. https://arxiv.org/abs/1112.6209
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
LeCun, Y. et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1, 541–551.
Louppe, G., Cho, K., Becot, C., & Cranmer, K. (2017). QCD-Aware Recursive Neural Networks for Jet Physics. https://arxiv.org/abs/1702.00748
MacFarlane, A. (2017). Rudolf Carnap (1891-1970). Philosophy Now, 118. https://philosophynow.org/issues/118/Rudolf_Carnap_1891-1970
Mayo, D. G. (1996). Error and the Growth of Experimental Knowledge. Chicago: Chicago University Press.
Mcculloch, W. S. & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133.
Miller, A. (2014). Realism. Stanford Encyclopedia of Philosophy. http://plato.stanford.edu/entries/realism/
Plaut, E. (2018). From Principal Subspaces to Principal Components with Linear Autoencoders. https://arxiv.org/abs/1804.10253
Psillos, S. (1999). Scientific Realism: How Science Tracks Truth. Routledge.
———. (2016). The Realist Turn in the Philosophy of Science. http://philsci-archive.pitt.edu/12440/
Quine, W. V. O. (1969). Natural kinds. In Ontological Relativity and Other Essays (pp. 114–138). New York: Columbia University Press.
Reichenbach, H. (1938). Experience and Prediction. Chicago: University of Chicago Press.
———. (1940). On the Justification of Induction. The Journal of Philosophy, 37, 97–103.
Rochester, N., Holland, J. H., Habit, L. H., & Duda, W. L. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on Information Theory, 2, 80–93.
Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model For Information Storage And Organization. In T. Brain. (Ed.), Psychological Review. 65, 386–408.
Salmon, W. C. (1963). On Vindicating Induction. Philosophy of Science, 30, 252–261.
———. (1966). The Foundations of Scientific Inference. Pittsburgh: University of Pittsburgh Press.
———. (1991). Hans Reichenbach’s Vindication of Induction. Erkenntnis, 35, 99–122.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://arxiv.org/abs/1404.7828
Sellars, W. (1964). Induction as Vindication. Philosophy of Science, 31, 197–231.
Sider, T. (2011). Writing the Book of the World. Oxford: Oxford University Press.
Solomonoff, R. J. (1996). Does Algorithmic Probability Solve the Problem of Induction? In Information, Statistics and Induction in Science: Proceedings of the Conference, ISIS ’96. World Scientific. http://raysolomonoff.com/publications/isis96.pdf
———. (1997). The Discovery of Algorithmic Probability. Journal of Computer and System Sciences, 55, 73–88. http://raysolomonoff.com/publications/barc97.pdf
Theodoridis, S. & Koutroumbas, K. (2009). Pattern Recognition. London: Elsevier.
Turing, A. (2004). Intelligent Machinery. In B. J. Copeland (Ed.), The Esssential Turing (pp. 410–433). Oxford: Clarendon Press. (Originally written in 1948).
van Fraassen, B. (1980). The Scientific Image. Oxford: Oxford University Press.
Wald, A. (1943). Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large. Transactions of the American Mathematical Society, 54, 426–482.
Wang, H., Raj, B., & Xing, E. P. (2017). On the Origin of Deep Learning. https://arxiv.org/abs/1702.07800
Weintraub, R. (1995). What was Hume’s Contribution to the Problem of Induction? The Philosophical Quarterly, 45, 460–470.
Wilks, S. S. (1938). Large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9, 60–62.
Williamson, J. (2004). A dynamic interaction between machine learning and the philosophy of science. Minds and Machines, 14, 539–549.
———. (2009). Probabilistic theories of causality. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), The Oxford Handbook of Causation (pp. 185–212). Oxford: Oxford University Press.
———. (2010). The Philosophy of Science and its relation to Machine Learning. In M. M. Gaber (Ed.), Scientific Data Mining and Knowledge Discovery (pp. 77–89). Springer.
———. (2011). Objective Bayesianism, Bayesian conditionalisation and voluntarism. Synthese, 178, 67–85.

  1. Alai (2004).↩︎

  2. Aurisano, A. et al. (2016).↩︎

  3. Bengio (2009).↩︎

  4. Bensusan (2000).↩︎

  5. Bueno (2014).↩︎

  6. Button & Walsh (2016).↩︎

  7. Carnap (1945a).↩︎

  8. Carnap (1945b).↩︎

  9. Carnap (1947a).↩︎

  10. Carnap (1947b).↩︎

  11. Carnap (1950).↩︎

  12. Copeland & Proudfoot (1996).↩︎

  13. Cowan (1998).↩︎

  14. Cowan, Cranmer, Gross, & Vitells (2011).↩︎

  15. Cowan (2016).↩︎

  16. Cranmer (2015).↩︎

  17. David & Netanyahu (2015).↩︎

  18. Davies (1987).↩︎

  19. DiNardo (2009).↩︎

  20. Farley & Clark (1954).↩︎

  21. Freund & Schapire (1996).↩︎

  22. Fukushima (1980).↩︎

  23. Ghahramani (2015).↩︎

  24. Good (1988).↩︎

  25. Goodell (2016).↩︎

  26. Goodman (1955).↩︎

  27. Guest, D. et al. (2016).↩︎

  28. Hacking (2001).↩︎

  29. Hastie, Tibshirani, & Friedman (2009).↩︎

  30. Hennig (2015).↩︎

  31. Huang, Loy, & Tang (2016).↩︎

  32. Hume (2007).↩︎

  33. James (2006).↩︎

  34. Kendall (1946).↩︎

  35. Korb (2004).↩︎

  36. Krizhevsky, Sutskever, & Hinton (2012).↩︎

  37. Le, Q. V. and et al (2013).↩︎

  38. LeCun, Y. et al. (1989).↩︎

  39. LeCun, Bottou, Bengio, & Haffner (1998).↩︎

  40. LeCun, Bengio, & Hinton (2015).↩︎

  41. Louppe, Cho, Becot, & Cranmer (2017).↩︎

  42. MacFarlane (2017).↩︎

  43. Mayo (1996).↩︎

  44. Mcculloch & Pitts (1943).↩︎

  45. Miller (2014).↩︎

  46. Plaut (2018).↩︎

  47. Psillos (1999).↩︎

  48. Psillos (2016).↩︎

  49. Quine (1969).↩︎

  50. Reichenbach (1938).↩︎

  51. Reichenbach (1940).↩︎

  52. Rochester, Holland, Habit, & Duda (1956).↩︎

  53. Rosenblatt (1958).↩︎

  54. Salmon (1963).↩︎

  55. Salmon (1966).↩︎

  56. Salmon (1991).↩︎

  57. Schmidhuber (2015).↩︎

  58. Sellars (1964).↩︎

  59. Sider (2011).↩︎

  60. Solomonoff (1996).↩︎

  61. Solomonoff (1997).↩︎

  62. Theodoridis & Koutroumbas (2009).↩︎

  63. Turing (2004).↩︎

  64. Wald (1943).↩︎

  65. Wang, Raj, & Xing (2017).↩︎

  66. Weintraub (1995).↩︎

  67. Wilks (1938).↩︎

  68. Williamson (2004).↩︎

  69. Williamson (2009).↩︎

  70. Williamson (2010).↩︎

  71. Williamson (2011).↩︎

  72. van Fraassen (1980).↩︎