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The conclusion of the Laplace approximation is that you can replace the environment variables in F with the mean of your recognition distribution. With the Dirac delta, this is as simple as (taken from the Bogacz tutorial):pic.twitter.com/CzPuJhQdUE
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I agree that the "value" should refer to the probability of a policy, but the figure shows deontic value as the likelihood of an observation given a policy, no?pic.twitter.com/l7Ni8DNKxs
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The equation says "the log likelihood of an observation given a policy", whereas the words says "the likelihood of a policy given an observation"pic.twitter.com/3R7Wq0FkC3
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I think that the simplest derivation from free energy would be this (where 'epistemic' scores the information gain and 'reward' scores the agreement between expected & prior (i.e. favourable) beliefs):pic.twitter.com/Wyp9Om00nN
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Indeed you have - compare equation 3 from your paper (attached in different notation) to equation 3 of https://www.fil.ion.ucl.ac.uk/~karl/Active%20inference%20and%20epistemic%20value.pdf …pic.twitter.com/7JFGqcWSi1
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"After analysing the OpenAI Five network it becomes clear that most of the network is dedicated to perception (preprocessing the observations) and motor control (decoding the actions). All the strategy and tactics must lie in one place – 1024-unit LSTM." https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five/ …pic.twitter.com/7dye6gKHVF
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The framework espoused by Clark & others (predictive coding) relies on the same objective function as variational auto-encoders:pic.twitter.com/h9LN6FMzfk
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