A tutorial on the free-energy framework for modelling perception and learning
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Re: A tutorial on the free-energy framework for modelling perception and learning
#2[deleted]
Re: A tutorial on the free-energy framework for modelling perception and learning
#3Anybody familiar with this method? It looks very intriguing as it looks to be combinging Bayesian posteriors with the neural activation function. Haven’t had time to dig into myself, and it would be nice to know an outside perspective on it.
Re: A tutorial on the free-energy framework for modelling perception and learning
#4That’s not how the brain works
The proposed method is wasteful in terms of energy spent to get an answer, specifically in this step : ‘ sum the whole range of possible sizes‘, even with approximations and clever algo
Perception is much more economical as it’s done via memorized heuristics that restrict the search space very quickly.
As a rule of thumb, If your method requires many iterations to converge on some minimum it’s a wrong method to model perception. Brain doesn’t solve a mathematical optimization problem.
Re: A tutorial on the free-energy framework for modelling perception and learning
#5For the probabilistic graphical model and belief prop perspective, check out Friston et al. (2017) "The graphical brain: Belief propagation and active inference": https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_0001...
For the neural corollaries of predictive coding, check out Shipp (2016) "Neural Elements for Predictive Coding": https://www.frontiersin.org/articles/10.3389/fpsyg.2016.0179...
For a state-of-art CV framework that fits with the free energy principle, check out the Recursive Cortical Network from George et al. (2017) "A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs ": http://science.sciencemag.org/content/358/6368/eaag2612
Re: A tutorial on the free-energy framework for modelling perception and learning
#6This is a pretty site. Anybody know how they did the code snippets?
Re: A tutorial on the free-energy framework for modelling perception and learning
#7That’s not how the brain works The proposed method is wasteful in terms of energy spent to get an answer, specifically in this step : ‘ sum the whole range of possible sizes‘, even with approximations and clever algo Perception is much more economical as it’s done via memorized heuristics that restrict the search space very quickly. As a rule of thumb, If your method requires many iterations to converge on some minim…
> The proposed method is wasteful in terms of energy spent to get an answer, specifically in this step : ‘ sum the whole range of possible sizes‘,
Er, the entire approach is motivated by the fact computing p(u) is intractable. That summation is explicitly not done in active inference...
Re: A tutorial on the free-energy framework for modelling perception and learning
#8This is a pretty site. Anybody know how they did the code snippets?
https://mmistakes.github.io/minimal-mistakes/
And looks like the content is in markdown.
Re: A tutorial on the free-energy framework for modelling perception and learning
#9Re: A tutorial on the free-energy framework for modelling perception and learning
#10> The non-linear function that relates size v to photosensory input u is assumed to be g(v)=v^2
I am having a hard time understanding this sentence - how does g(v) = v^2 relate the size v and the input u if the expression mentions only v?
Is it meant to be v = g(u) = u^2? Is it u = g(v) = v^2?