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Results tagged with gradient-descent
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user 115837
Gradient descent is a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of the approximate gradient) of the function at the current point. For stochastic gradient descent there is also the [sgd] tag.
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Updating Prediction Errors in Gradient Ascent (Friston's Free-energy)
Background
In Rafal Bogacz's tutorial on the free-energy framework for modelling perception and learning, section 2.3 we have:
$$\dot{\phi} = \frac{\partial F}{\partial\phi} = \varepsilon_u g'(\phi) …
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1
vote
Updating Prediction Errors in Gradient Ascent (Friston's Free-energy)
OK, I've worked it out. Here's the most trivial example I could think of.
$$ I = \frac{V}{R}$$
which can be rearranged:
$$ IR = V$$
So in:
$$ \dot{I} = V - IR$$
$V$ stands for the the actual vol …
- 213