Perception as Bayesian Inference

2014

474 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/eeb0da4b82d7bf0f60901f9bbb34b46e780c1849

Citations in the corpus

• Rational quantitative attribution of beliefs, desires and percepts in human mentalizing (2017, unknown): “Our goal is a formal, computational account, analogous in scope and explanatory power to computational accounts of visual perception 1–3 that represent some of the greatest successes of model-building in cognitive science.”
• A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts (2015, unknown): “The transformation F that imposes the Efficient coding constraint determines how the stimulus noise is mapped to the sensory space (equation (8)).”
• The Human Kernel (2015, unknown): “In recent years, probabilistic modelling has become a cornerstone of machine learning approaches [2, 3, 4], with applications in neural processing [5, 6, 3, 7] and human learning [8, 9].”
• Multistability and Perceptual Inference (2012, unknown): “As Published http://dx.doi.org/10.1162/NECO_a_00226 Publisher MIT Press Version Final published version Citable link http://hdl.handle.net/1721.1/70125 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law.”
• Probabilistic brains: knowns and unknowns (2013, unknown): “These experiments have shown that human behavior is highly consistent with probabilistic reasoning not only in the sensory domain 8–12 , but also in the motor 13–15 and cognitive 16–22 domains.”
• OpenDR: An Approximate Differentiable Renderer (2014, unknown): “In the standard Bayesian formulation, the likelihood function specifies the forward rendering process, while the prior constrains (or regularizes) the space of models or parameters [19].”
• Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making. (2012, unknown): “Introduction Efficient decision-making requires inferring the state of the world from uncertain or ambiguous evidence [1].”
• Probabilistic machine learning and artificial intelligence (2015, unknown): “…as the theoretical foundation for rationality in artificial intelligence systems, but also as a model for normative behaviour in humans and animals 15–18 (but see refs 19, 20 for a discussion), and much research is devoted to understanding how neural circuitry may be implementing Bayesian…”