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What are the "tricks" in machine learning? [closed]

I have come across a few different "tricks" in machine learning methodology, which I list below along with my rudimental understandings. The Kernel Trick: This is used in Support Vector ...
camhsdoc's user avatar
  • 409
2 votes
1 answer

How to do log subtract (just like logsumexp) with probabilities? [closed]

To subtract a small probability from another, this answer has constraint on log probabilities l1 > l2: Subtracting very small probabilities - How to compute? but I need a function that works for ...
monotonic's user avatar
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3 votes
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Dummy variables in regression with log-transformed continous variables

I have a question dealing with logarithmic variables, log-sum and dummy variables in a multiple regression. First of all I’ve never worked with log-sums before. I have a model with y that is log-...
Diego's user avatar
  • 31
3 votes
1 answer

parallelizing log-sum-exp

I have some approximate likelihoods: $L_1, \ldots, L_n$. Each is quite expensive to calculate. They're approximate because they use random numbers. Each of them is being calculated on the same data ...
Taylor's user avatar
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8 votes
3 answers

log(1 - softmax(X))? [closed]

Let $\vec X$ be a vector. The $\vec V = \mathrm{logsoftmax}(\vec{X})$ function is defined as: $$v_i = \ln\left(\frac{e^{x_i}}{\sum_i e^{x_i}}\right)$$ This is provided in machine learning numerical ...
a06e's user avatar
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3 votes
1 answer

Vectorised computation of logsumexp

In this related post there is an explanation of how you can add together two very small probabilities using the logsumexp function, and how this can be programmed into base ...
Ben's user avatar
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3 votes
1 answer

What is the use of the log of the sum of exponents in machine learning

I want to understand why somebody would use log sum exponent trick. I am reading this blog. But I don't really understand the first paragraph. It says Let's say we have an n-dimensional ...
Funzo's user avatar
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