The human kernel

Advances in Neural Information Processing Systems

Volumn 2015-January, Issue , 2015, Pages 2854-2862

  Wilson, Andrew Gordon ^a   Dann, Christoph ^a   Lucas, Christopher G ^b   Xing, Eric P ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EXTRAPOLATION; GAUSSIAN NOISE (ELECTRONIC); INFORMATION SCIENCE;

BEHAVIORAL EXPERIMENT; DEGREE OF FLEXIBILITY; FUNCTION LEARNING; GAUSSIAN PROCESSES; NON-PARAMETRIC MODEL; POLYNOMIAL KERNELS; PSYCHOLOGICAL INSIGHTS; STATISTICAL MODELLING;

GAUSSIAN DISTRIBUTION;

EID: 84965135857     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. W. S. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biology, 5(4):115-133, 1943.
2. Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
3. K. Doya, S. Ishii, A. Pouget, and R. P. N. Rao. Bayesian brain: probabilistic approaches to neural coding. MIT Press, 2007.
4. Zoubin Ghahramani. Probabilistic machine learning and artificial intelligence. Nature, 521(7553):452-459, 2015.
5. Daniel M Wolpert, Zoubin Ghahramani, and Michael I Jordan. An internal model for sensorimotor integration. Science, 269(5232):1880-1882, 1995.
6. David C Knill and Whitman Richards. Perception as Bayesian inference. Cambridge University Press, 1996.
7. Sophie Deneve. Bayesian spiking neurons i: inference. Neural computation, 20(1):91-117, 2008.
8. Thomas L Griffiths and Joshua B Tenenbaum. Optimal predictions in everyday cognition. Psychological Science, 17(9):767-773, 2006.
9. J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman. How to grow a mind: Statistics, structure, and abstraction. Science, 331(6022):1279-1285, 2011.
10. R. M. Neal. Bayesian Learning for Neural Networks. Springer Verlag, 1996. ISBN 0387947248.
11. C. E. Rasmussen and C. K. I. Williams. Gaussian processes for Machine Learning. MIT Press, 2006.
12. Andrew Gordon Wilson. Covariance kernels for fast automatic pattern discovery and extrapolation with Gaussian processes. PhD thesis, University of Cambridge, 2014. http://www.cs.cmu.edu/~andrewgw/andrewgwthesis.pdf.
13. Tommi Jaakkola, David Haussler, et al. Exploiting generative models in discriminative classifiers. Advances in neural information processing systems, pages 487-493, 1998.
14. Andrew Gordon Wilson and Ryan Prescott Adams. Gaussian process kernels for pattern discovery and extrapolation. International Conference on Machine Learning (ICML), 2013.
15. J Douglas Carroll. Functional learning: The learning of continuous functional mappings relating stimulus and response continua. ETS Research Bulletin Series, 1963(2), 1963.
16. Kyunghee Koh and David E Meyer. Function learning: Induction of continuous stimulus-response relations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17(5):811, 1991.
17. Edward L De Losh, Jerome R Busemeyer, and Mark A McDaniel. Extrapolation: The sine qua non for abstraction in function learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(4):968, 1997.
18. Jerome R Busemeyer, Eunhee Byun, Edward L Delosh, and Mark A McDaniel. Learning functional relations based on experience with input-output pairs by humans and artificial neural networks. Concepts and Categories, 1997.
19. Thomas L Griffiths, Chris Lucas, Joseph Williams, and Michael L Kalish. Modeling human function learning with Gaussian processes. In Neural Information Processing Systems, 2009.
20. Christopher G Lucas, Thomas L Griffiths, Joseph J Williams, and Michael L Kalish. A rational model of function learning. Psychonomic bulletin & review, pages 1-23, 2015.
21. Christopher G Lucas, Douglas Sterling, and Charles Kemp. Superspace extrapolation reveals inductive biases in function learning. In Cognitive Science Society, 2012.
22. Mark A Mcdaniel and Jerome R Busemeyer. The conceptual basis of function learning and extrapolation: Comparison of rule-based and associative-based models. Psychonomic bulletin & review, 12(1):24-42, 2005.
23. Michael L Kalish, Thomas L Griffiths, and Stephan Lewandowsky. Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin & Review, 14(2):288-294, 2007.
24. Daniel R Little and Richard M Shiffrin. Simplicity bias in the estimation of causal functions. In Proceedings of the 31st Annual Conference of the Cognitive Science Society, pages 1157-1162, 2009.
25. Samuel GB Johnson, Andy Jin, and Frank C Keil. Simplicity and goodness-of-fit in explanation: The case of intuitive curve-fitting. In Proceedings of the 36th Annual Conference of the Cognitive Science Society, pages 701-706, 2014.
26. Samuel J Gershman, Edward Vul, and Joshua B Tenenbaum. Multistability and perceptual inference. Neural computation, 24(1):1-24, 2012.
27. Thomas L Griffiths, Edward Vul, and Adam N Sanborn. Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21(4):263-268, 2012.
28. Edward Vul, Noah Goodman, Thomas L Griffiths, and Joshua B Tenenbaum. One and done? optimal decisions from very few samples. Cognitive science, 38(4):599-637, 2014.
29. Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, and John P. Cunningham. Fast kernel learning for multidimensional pattern extrapolation. In Advances in Neural Information Processing Systems, 2014.
30. David JC MacKay. Information theory, inference, and learning algorithms. Cambridge U. Press, 2003.
31. Carl Edward Rasmussen and Zoubin Ghahramani. Occam's razor. In Neural Information Processing Systems (NIPS), 2001.
32. Andrew Gordon Wilson. A process over all stationary kernels. Technical report, University of Cambridge, 2012.