For instance, Yuille and Bülthoff [44] describe the Bayesian approach to perception in terms of faithful depiction: “We define vision as perceptual inference, the estimation of scene properties from an image or sequence of images . . . ther…44. Yuille, A., and Bülthoff, H. (1996). Bayesian decision theory and psychophysics, in Perception as Bayesian inference, ed. D. Knill and W. Richards (Cambridge University Press, Cambridge).
45. Zollman, K. (2005). Talking to neighbors: T…
Alan Yuille
scientist · 2 mentions across 1 reading
In this course
Alan Yuille is a computational neuroscientist known for formalizing vision as Bayesian inference—treating perception as a statistical problem of estimating scene properties from incomplete sensory data. In the course readings, his work grounds theoretical discussions of how machines and minds model uncertainty, enabling arguments about perception as probabilistic inference rather than direct representation. His framing becomes crucial for understanding how AI systems approximate human perceptual reasoning through similar Bayesian frameworks.
Mentioned in 1 reading
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