Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
By Lei Shi, T. Griffiths. 2009
94 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sergey Levine
- Yoshua Bengio
- Josh Tenenbaum
- Lei Tai
- Pieter Abbeel
- Chelsea Finn
- Trevor Darrell
- Ruslan Salakhutdinov
Citations in the corpus
- The limits and potentials of deep learning for robotics (2018, background): “…re-composition of component solutions from different places on the spectrum can be achieved with differentiable versions of existing algorithms (one end of the spectrum) that are compatible solutions obtained with back-propagation (other end of the spectrum) [16], [39], [50], [108], [116], [128].”
- Multistability and Perceptual Inference (2012, background): “, 2009) and animal (Daw and Courville, 2007) cognition, and several authors have proposed that the spiking activity of neurons might be interpreted as a set of samples (Hoyer and Hyvärinen, 2003; Lee and Mumford, 2003; Shi and Griffiths, 2009; Fiser et al., 2010).”
- Mirroring and beyond: coupled dynamics as a generalized framework for modelling social interactions (2016, methodology): “This approach has been used to model visual perception in the brain [66] and also to model how people can infer the intentions of others [67].”
- Bridging Levels of Analysis for Probabilistic Models of Cognition (2012, background): “Recent papers have shown how simple neural circuits can implement algorithms such as importance sampling (Shi & Griffiths, 2009) and how variability in neural responding might be interpreted in terms of sampling (Fiser, Berkes, Orban, & Lengyel, 2010).”
- The Visual System's Internal Model of the World (2015, background): “[76] L. Shi and T. L. Griffiths, ‘‘Neural implementation of hierarchical Bayesian inference by importance sampling,’’ Adv.”
- Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons (2011, background): “There are a number of studies proposing neural network architectures that implement probabilistic inference [15,17,18,35– 48].”