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< rcurtin> wasiq: it would be good to read those papers and be familiar with SGD-type approaches
< rcurtin> the Hogwild! paper may also be useful
< rcurtin> (and you could take a look at other more recent papers that cite the Hogwild! paper)
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< travis-ci> awhitesong/mlpack#3 (master - 8ffa470 : marcus): The build failed.
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< zoq> okay...
< rcurtin> yeah, I was wondering the same thing
< rcurtin> I haven't heard from awhitesong in over a year, I wonder why travis decided to build his branch
< zoq> and sends a messesage to this channel
< rcurtin> bizarre
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< wasiq> rcurtin, alright. So basically SGD takes a random value of the input set as parameter and manages to retain a reasonable consistency in output,but with a increased error probability?
< rcurtin> yes, it's a stochastic optimizer
< rcurtin> it's not necessarily right to say that the error probability is increased, though
< rcurtin> there has been a lot of theoretical analysis of SGD-like algorithms (again the Hogwild! paper has some good analysis and links to other analyses)
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< wasiq> okay.. Ill read up on it.
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< abdelhadiTEMMAR> hi ! i'm Abdelhadi TEMMAR :)
< abdelhadiTEMMAR> so, how many people use this library today ?
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< abdelhadiTEMMAR> i'm interested in the neuroevolution project. I was wodering if stochastic gradient descent and CNE are synonyms ? Because "simple weight evolution over a topologically static neural network." seems to be definition of SGD for me
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< tugu> I want to work on the open source #mlpack and have a good knowledge of machine learning. I want to know the criterion that you judge in a potential candidate.
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< travis-ci> awhitesong/mlpack#4 (Activationlayers - 356d954 : Dhawal Arora): The build failed.
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< Guest26255> hello
< Guest26255> i am getting this link error
< Guest26255> g++ 2.cpp -lmlpack -std=c++11 /usr/bin/ld: /tmp/ccM52eLV.o: undefined reference to symbol '_ZTIN5boost13serialization13typeid_system27extended_type_info_typeid_0E' //usr/lib/x86_64-linux-gnu/libboost_serialization.so.1.54.0: error adding symbols: DSO missing from command line collect2: error: ld returned 1 exit status
< Guest26255> can anybody help me ?
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< acbull> Hello. Something run with the compiling.
< acbull> src/mlpack/CMakeFiles/mlpack.dir/build.make:629: recipe for target 'src/mlpack/CMakeFiles/mlpack.dir/methods/fastmks/fastmks_model.cpp.o' failed make[2]: *** [src/mlpack/CMakeFiles/mlpack.dir/methods/fastmks/fastmks_model.cpp.o] Error 4 CMakeFiles/Makefile2:152: recipe for target 'src/mlpack/CMakeFiles/mlpack.dir/all' failed make[1]: *** [src/mlpack/CMakeFiles/mlpack.dir/all] Error 2 Makefile:117: recipe for target 'all' failed make: ***
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< anveshi> Guest26255: I faced similar issue yesterday. It was resolved using -lboost_serialization during compilation
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< treq> Hello, i need help when making the mlpack
< treq> src/mlpack/CMakeFiles/mlpack.dir/build.make:629: recipe for target 'src/mlpack/CMakeFiles/mlpack.dir/methods/fastmks/fastmks_model.cpp.o' failed make[2]: *** [src/mlpack/CMakeFiles/mlpack.dir/methods/fastmks/fastmks_model.cpp.o] Error 4 CMakeFiles/Makefile2:152: recipe for target 'src/mlpack/CMakeFiles/mlpack.dir/all' failed make[1]: *** [src/mlpack/CMakeFiles/mlpack.dir/all] Error 2 Makefile:117: recipe for target 'all' failed make: ***
< treq> running on virtual machine of ubuntu 15.04
< treq> Is there anyone here can help? Just don't know what the mistake is.
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< anveshi> I am getting this error http://pastebin.com/Ei7B0tKN while compiling tree_test.cpp file
< anveshi> any idea, What went wrong ?
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< anjan_> I am a CSE fresher.I want to contribute to MLpach through GSOC.From where shall I start?
< anjan_> *pack
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< travis-ci> awhitesong/mlpack#5 (Activationlayers - 4a15114 : Dhawal Arora): The build is still failing.
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< yvtheja> ank_95_, You look at ideas page first :)
< yvtheja> <yvtheja> ank_95_, You canlook at ideas page first :)
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< ank_95_> I have looked at it and find the project "Fast k-centers Algorithm & Implementation" interesting, Can you tell how should I proceed.
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< Jayaganesh> ank_95, I'm another contributor like u. U need to probably start off with "Dual-Tree Boruvka MST algorithm" and its implementation in MLpack. And even before that u'd like to read "Scalable Sparse Approximation of a Sample Mean" by Cortes and Scott-2013. U may start off with these. hope these helped u.
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< ranjan123> hii
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< asnel_ulrich> Hello,
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< ranjan123> hello
< ranjan123> is there anybody help me about sgd
< ranjan123> ?
< rcurtin> hi ranjan123, what do you need help with?
< ranjan123> yes
< ranjan123> Idea was to implement sgd parallel.
< ranjan123> with using pthread ?
< ranjan123> lib
< ranjan123> ?
< rcurtin> what is your question?
< ranjan123> i mean we are suppoed to implement the the algo 3 in this paper
< ranjan123> Algorithm 3
< ranjan123> right ?
< ranjan123> ?
< ranjan123> ?
< rcurtin> please be patient for an answer. I have to answer tens of emails and lots of bug reports
< rcurtin> you are not "supposed" to do anything in particular; your project proposal is up to you
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< rcurtin> what you've suggested (Algorithm 3) is one possibility, but it is not the only possibility for what you could do for this project
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< ranjan123> and how can I test the sequential sgd ?
< rcurtin> you can build mlpack_test (`make mlpack_test`) and run the test: `bin/mlpack_test -t SGDTest`
< rcurtin> for the code that is used for testing, see src/mlpack/tests/sgd_test.cpp
< ranjan123> hmm. thank you !
< rcurtin> sure, glad I could be helpful
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< Rishabh> rcurtin: Hi, I wish to apply for GSoC 2016 for the project Implement tree types. Since the project involves implementing different variations of tree data structure, I want to start contributing by implementing one of the types of tree enlisted under the project description.
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< rcurtin> hi Rishabh, you are more than welcome to do that
< Rishabh> rcurtin: I had a question - How do we benchmark the runtimes of different dual tree algorithms ?
< Rishabh> Is the relevant code under mlpack/tests/ ?
< rcurtin> there is a benchmarking system at https://github.com/zoq/benchmarks
< rcurtin> but otherwise you can benchmark them by setting up scripts to run the algorithms with different parameters
< rcurtin> you can just pick some UCI datasets or something like this
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< Rishabh> rcurtin: Great. Let me try the benchmarking tool first with the already implemented trees.
< Rishabh> rcurtin: Thanks
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< ranjan123> can anyone help me to understand this --.> BOOST_REQUIRE_CLOSE(a,b,c)
< ranjan123> ?
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< ranjan123> thans
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< zoq_> kirizaki: I'm about to test the code on freebsd.
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< kirizaki> ok, great but it doesn't change from last time
< kirizaki> some #ifdefs has been added but this shouldn't help at all
< kirizaki> I forgot about it :F
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< zoq> On FreeBSD, libdl is part of libc so FindLibDL always 'fails' but that's easy to fix. Let's see how far I can get with FreeBSD.
< zoq> Btw. whats the reason for the uncomment of LIBBFD_IBERTY_LIBRARY and LIBBFD_OPCODES_LIBRARY in https://github.com/Kirizaki/mlpack/blob/master/CMake/FindBfd.cmake
< Rishabh> hi, I was going through the code in core/tree/rectangle_tree/rectangle_tree.hpp . Rectangle tree has parameters maxLeafSize = 20,minLeafSize = 8, maxNumChildren = 5 and minNumChildren = 2. Can't understand why, am I missing out on something ?
< kirizaki> @zoq: mlpack::backtrace doesn't need LIBBFD_IBERTY_LIBRARY and LIBBFD_OPCODES_LIBRARY in Linux
< rcurtin> Rishabh: those are just defaults, they may not be the best for any given situation but they work okay
< kirizaki> @zoq: including those libs was throwing some errors, I don't remember now what was it exactly
< kirizaki> @zoq: do You want me to reproduce it?
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< zoq> kirizaki: I shouldn't make a difference to include LIBBFD_IBERTY_LIBRARY and LIBBFD_OPCODES_LIBRARY as long as you don't use it. No need to reproduce the error.
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< travis-ci> awhitesong/mlpack#6 (LeakyReLU - eab7794 : Dhawal Arora): The build passed.
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< travis-ci> awhitesong/mlpack#7 (HardTanH - 0a68185 : Dhawal Arora): The build failed.
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