Abstract. We discuss a general technique that forms a differentiable bound on non-differentiable objective functions by bounding the function.
Dec 18, 2012 · We discuss a general technique that can be used to form a differentiable bound on the optima of non-differentiable or discrete objective functions.
We discuss a general technique that forms a differentiable bound on non-differentiable objective functions by bounding the function optimum by its ...
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We first describe inference with PGMs and the intractability of exact inference. • Then give a taxonomy of inference algorithms.
The used variational distributions are very flexible and we show that evolutionary algorithms can effectively and efficiently optimize the variational bound.
Missing: Bounding. | Show results with:Bounding.
A general technique that can be used to form a differentiable bound on the optima of non-differentiable or discrete objective functions.
Oct 20, 2023 · Variational inference provides a way to approximate probability densities through optimization. It does so by optimizing an upper or a lower ...
The variational method that we have described involves replacing selected local conditional probabilities with either upper-bounding or lower-bounding ...
In this chapter, we are going to look at an alternative approach to approximate inference called the variational family of algorithms.
Missing: Bounding. | Show results with:Bounding.
Bounding can be used to compute upper or lower bounds on functions, approximation errors, etc. to tackle intractable maximization or minimization problems.