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/
< ShikharJ>
Toshal: Please re-use the commits from the Label Smoothing branch, if it's urgent. I'll provide a review as soon as I can.
< ShikharJ>
Toshal: You can work on the template code for the LSGANs, I think. Label Smoothing would require serialization, which I'm scouring through right now. Hopefully it'll be done soon.
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< zoq>
gotadachi: Not sure wht the new location is, Ryan might have an idea.
< zoq>
gotadachi: https://www.ratml.org/misc/datasets.tar.gz should contain the dataset you are searching for, but if you can wait some more time, there might be an option to only load the dataset you need.
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< gotadachi>
zoq: Thank you for the helpful information. I'll try later.
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< gotadachi>
(take a bit more time to download
< zoq>
gotadachi: Right, also needs more space.
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< gotadachi>
zoq: OK. I got the dataset. tutorial was run ideally.
< rcurtin>
ah, why isn't it there? that's strange
< gotadachi>
zoq: Thank you so much!
< rcurtin>
the build process must not be right. let me work on that, but for now I re-unpacked everything into mlpack.org/datasets/
< rcurtin>
gotadachi: thanks for the report... I would not have noticed this otherwise
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< sakshamB>
Toshal: need them for computation
< xiaohong>
zoq: Hi, is there any solution that my model outputs two dimension for construct GaussianDistribution.
< xiaohong>
But I use the GaussianDistribution to compute loss with one dimension, and then backward?
< xiaohong>
It seems that the dimension is not compatiable.
< zoq>
xiaohong: With the GaussianDistribution., I don't think you can do that, you would have to write your own.
< xiaohong>
Okay, I see.
< xiaohong>
Are the our GaussianDistribution definition the same with tensorflow?
< xiaohong>
It seems that our GaussianDistribution accepts the mean and covariance as parameters.
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< zoq>
xiaohong: Similair, if you write your own, you can do what tf does.
< xiaohong>
zoq: Thank you~ I got it.
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< abhikanaserocks>
Hi !!
< rcurtin>
abhikanaserocks: hi there!
< abhikanaserocks>
I am Abhijeet Kanase,
< abhikanaserocks>
If you could guide me through the process that how to contribute and how did you prepare for getting mlpack would be really helpful for me!!!
< abhikanaserocks>
I dont know to contribute to OSS and I haven't used GIT/Linux as well
< rcurtin>
in this case, you might consider taking a look at other online guides and searching for some more basic guides and tutorials
< rcurtin>
unfortunately I don't have any handy for you, but a search should find some :)
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< abernauer>
rcurtin: Is updated or more robust documentation on CMake, specifically on the github wiki an area of need?
< rcurtin>
abernauer: that's always an area of need :) if you see something incorrect we should definitely change it
< abernauer>
Brought it up because, while you were on vacation, I picked up an ebook by the CMake co-project maintainer.
< abernauer>
Took notes and plan on taking more has decent examples, for more complicated projects and gets into the details more. Even a chapter on using openmp.