ChanServ changed the topic of #mlpack to: "mlpack: a fast, flexible machine learning library :: We don't always respond instantly, but we will respond; please be patient :: Logs at http://www.mlpack.org/irc/
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< Hemal>
Hello, I've a doubt regarding one of the parameters in the loss functions of ann. In /mlpack/src/mlpack/methods/ann/loss_functions, what is 'target'? Is the class 'Y' ? Does it have a conventional meaning?
< Hemal>
I've seen the word 'target' in almost every file under the folder which path i mentioned
< Hemal>
I am implementing the triplet loss function, where I have 3 input params: the anchor, the positive example, and the negative example ..... So should I treat the 'anchor' as a target, or should the anchor be 'const InputType&& anchor'
< Hemal>
(triplet loss function for the siamese network)
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< ShikharJ>
rcurtin: zoq: I have added a few bits to the testing guidelines wiki page. I have left a couple of sections on Creating Tests (Using Boost framework) and Analyzing and Reporting Errors, since I guessed I might not be the best person to write on those topics.
< ShikharJ>
Do give a read and let me know how you feel. I have tried to include examples wherever applicable, and didn't make it too detailed for ease of readability.
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< zoq>
Hemal: In your case you have to encode the anchor,postive,negative as input (arma::cube) and since there is no target, it's empty.
< zoq>
ShikharJ: Great, will take a loook once I get a chance.
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< riaash04>
Hi, I am interested in the NEAT project and did go through the open PRs related to that. But, I could not get anywhere from that. Any suggestions regarding how to go forward? Thanks.
< zoq>
riaash04: Hello there, have you seen http://www.mlpack.org/involved.html or https://www.mlpack.org/gsocblog/? Also for the NEAT project, it's a plus to be familiar with the optimization framework (ensmallen), one idea might be extend an existing method or to implement a new one. Hopefully anything I said was helpful.
< riaash04>
zoq: Thanks for the reply :) I have seen those pages and read about ensmallen. Though I still need to get familiar with it. I will do that now. Also, if you know any more resources (besides the orignal paper on NEAT) to study about NEAT or how to start with implementation of an algorithm in mlpack, that would be great help. :)
< zoq>
riaash04: Also The HyperNEAT paper might give some additional insights as well.
< riaash04>
zoq: Thanks for the help. :)
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< dinesh>
I am here to know more about the project. Can any one share the resources and documentation of the project.
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< KimSangYeon-DGU>
rcurtin: Hi, rcurtin. I have made some test cases for DiagonalGMM & HMMs and now I will measure performance. After the performance measurement is done, I will post summary that shows what is changed in the PR #1666.
< rcurtin>
KimSangYeon-DGU: sounds great!
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