mlpack IRC logs, 2017-05-10

Logs for the day 2017-05-10 (starts at 0:00 UTC) are shown below.

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--- Log opened Wed May 10 00:00:00 2017
01:08 -!- benchmark [~benchmark@198-0-35-42-static.hfc.comcastbusiness.net] has joined #mlpack
01:08 -benchmark:#mlpack- FastMKS (-k 3 --kernel linear) | optdigits 4.54 (old) => 4.52 (new) => -0.03 (diff) | cloud 0.03 (old) => 0.03 (new) => -0.00 (diff) |
01:08 -benchmark:#mlpack- FastMKS (-k 3 --kernel gaussian) | optdigits 16.26 (old) => 15.99 (new) => -0.27 (diff) | cloud 1.57 (old) => 1.54 (new) => -0.03 (diff) |
01:08 -benchmark:#mlpack- FastMKS (-k 3 --kernel linear --single) | optdigits 4.52 (old) => 4.56 (new) => 0.04 (diff) | cloud 0.03 (old) => 0.03 (new) => -0.00 (diff) |
01:08 -benchmark:#mlpack- FastMKS (-k 3 --kernel gaussian --single) | optdigits 12.97 (old) => 12.70 (new) => -0.27 (diff) | cloud 1.13 (old) => 1.11 (new) => -0.02 (diff) |
01:08 -benchmark:#mlpack- FastMKS (-k 3 --kernel polynomial --degree 2 --single) | optdigits 17.80 (old) => 17.63 (new) => -0.17 (diff) | cloud 0.86 (old) => 0.85 (new) => -0.01 (diff) |
01:08 -benchmark:#mlpack- FastMKS (-k 3 --kernel polynomial --degree 2) | optdigits 20.89 (old) => 20.58 (new) => -0.30 (diff) | cloud 0.29 (old) => 0.29 (new) => -0.00 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --alpha 0.9) | covtype 461.59 (old) => 461.59 (new) => -0.00 (diff) | corel-histogram 7.72 (old) => 7.71 (new) => -0.02 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --tree_type=cover) | covtype 560.73 (old) => 560.41 (new) => -0.32 (diff) | corel-histogram 31.32 (old) => 31.20 (new) => -0.12 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --sample_at_leaves) | covtype 316.26 (old) => 315.92 (new) => -0.34 (diff) | corel-histogram 7.27 (old) => 7.25 (new) => -0.01 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --first_leaf_exact) | covtype 458.79 (old) => 457.71 (new) => -1.08 (diff) | corel-histogram 7.86 (old) => 7.86 (new) => -0.00 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --tree_type=cover --single_mode) | covtype 443.61 (old) => 445.00 (new) => 1.39 (diff) | corel-histogram 28.45 (old) => 27.87 (new) => -0.58 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --first_leaf_exact --single_mode) | covtype 205.91 (old) => 205.79 (new) => -0.13 (diff) | corel-histogram 8.39 (old) => 8.41 (new) => 0.03 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3) | covtype 458.69 (old) => 457.83 (new) => -0.85 (diff) | corel-histogram 7.85 (old) => 7.84 (new) => -0.01 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --sample_at_leaves --single_mode) | covtype 22.89 (old) => 22.67 (new) => -0.22 (diff) | corel-histogram 4.26 (old) => 4.25 (new) => -0.01 (diff) |
01:08 -benchmark:#mlpack- ALLKRANN (-k 3 --alpha 0.9 --tau 10) | covtype 459.95 (old) => 459.45 (new) => -0.51 (diff) | corel-histogram 7.33 (old) => 7.33 (new) => 0.01 (diff) |
01:08 -benchmark:#mlpack- ALLKNN (-k 3 --tree_type=cover) | covtype 181.07 (old) => 181.16 (new) => 0.09 (diff) | corel-histogram 113.03 (old) => 112.75 (new) => -0.28 (diff) |
01:08 -benchmark:#mlpack- ALLKNN (-k 3) | covtype 14.81 (old) => 14.80 (new) => -0.01 (diff) | corel-histogram 13.04 (old) => 13.17 (new) => 0.12 (diff) |
01:08 -benchmark:#mlpack- ALLKNN (-k 3 --single_mode --tree_type=cover) | covtype 326.79 (old) => 326.15 (new) => -0.63 (diff) | corel-histogram 190.03 (old) => 187.90 (new) => -2.13 (diff) |
01:08 -benchmark:#mlpack- LSH (-k 3 --seed 42 --tables 40) | cloud 0.05 (old) => 0.07 (new) => 0.01 (diff) | corel-histogram 16.75 (old) => 16.86 (new) => 0.11 (diff) |
01:08 -benchmark:#mlpack- LSH (-k 3 --seed 42) | cloud 0.04 (old) => 0.04 (new) => 0.00 (diff) | corel-histogram 14.19 (old) => 14.07 (new) => -0.12 (diff) |
01:08 -benchmark:#mlpack- LSH (-k 3 --seed 42 --bucket_size 50) | cloud 0.04 (old) => 0.04 (new) => -0.00 (diff) | corel-histogram 3.87 (old) => 3.84 (new) => -0.03 (diff) |
01:08 -benchmark:#mlpack- ALLKFN (-k 3 --tree_type=cover) | covtype 139.70 (old) => 139.58 (new) => -0.12 (diff) | corel-histogram 46.67 (old) => 46.60 (new) => -0.06 (diff) |
01:08 -benchmark:#mlpack- ALLKFN (-k 3) | covtype 11.94 (old) => 11.93 (new) => -0.01 (diff) | corel-histogram 9.52 (old) => 9.55 (new) => 0.03 (diff) |
01:08 -benchmark:#mlpack- ALLKFN (-k 3 --single_mode --tree_type=r) | covtype 904.59 (old) => 896.43 (new) => -8.16 (diff) | corel-histogram 392.82 (old) => 393.13 (new) => 0.31 (diff) |
01:08 -benchmark:#mlpack- ALLKFN (-k 3 --tree_type=r) | covtype -1.00 (old) => -1.00 (new) => 0.00 (diff) | corel-histogram 148.80 (old) => 149.44 (new) => 0.64 (diff) |
01:08 -benchmark:#mlpack- ALLKFN (-k 3 --single_mode --tree_type=cover) | covtype 173.33 (old) => 173.45 (new) => 0.12 (diff) | corel-histogram 51.93 (old) => 52.03 (new) => 0.10 (diff) |
01:08 -benchmark:#mlpack- Benchmarks 51 of 52 passed.
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14:01 < kris2> @lozhnikov you there
14:01 < kris2> :lozhnikov
14:05 < lozhnikov> kris2: hi
14:05 < kris2> Hi
14:06 < kris2> lozhnikov: just wanted to ask if you had gone through my proposal. Did you find the api okay.
14:06 < kris2> Also should i first implement RBM
14:06 < kris2> or GAN portion
14:09 < lozhnikov> I think it doesn't matter
14:09 < lozhnikov> As for me we have to correct the API
14:10 < kris2> I think stacked GAN's are pretty difficult. That's why i wanted to start with RBM's
14:11 < kris2> Ok, thats fine the API was just meant as a reference.
14:11 < lozhnikov> ok. I am not sure of your variant of gibbs sampling
14:13 < kris2> What exactly do you mean ?
14:15 < kris2> Gibbs distribution is just sampling from a conditional distribution P(x_i| x_1,x_2,.....x_i-1,x_i+1,.....x_n) right?
14:15 < lozhnikov> I mean the 10th page of your proposal
14:17 < lozhnikov> actually we have to sample sample hidden variables from visible and then visible variables from hidden k times
14:19 < kris2> Yes that's correct. Yes i see, my gibbs sampler implementation was more of a general gibbs sampler
14:20 < kris2> I will change that implementation to more RBM specific
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14:22 < kris2> I was also little unclear about block gibbs sampling
14:23 < kris2> I will try to read about it tonight i think. We would need that for implementing the Persistence-CD
14:24 < lozhnikov> I'll take a look at that.
14:24 < kris2> :lozhnikov do you have any links/ books that you want to me study before we start coding.
14:26 < lozhnikov> And I've got another question: It seems that your variant of RBM is not modular. As for me it is better to implement hidden and visible variables in terms of LayerType.
14:27 < lozhnikov> As for me an implementation at deeplearning.net could be very helpful
14:27 < lozhnikov> you could find that here http://deeplearning.net/tutorial/rbm.html
14:28 < lozhnikov> and here http://deeplearning.net/tutorial/DBN.html
14:28 < kris2> Hmmm, okay i have gone through that, i will look at it again.
14:30 < kris2> Will you be attending tomorrow meeting btw.
14:30 < lozhnikov> yeah, I hope I'll be at home
14:32 < kris2> cool, btw i know its a little late for a intro but still. i am masters student in iit hyderabad india. studying machine learning
14:32 < kris2> my focus right now is submodular functions in ml. Earlier i was working on graph kernels
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14:37 < lozhnikov> yeah, you pointed out that in your proposal:) did you work with neural networks before?
14:38 < kris2> Yes, i have quite a lot actually.
14:39 < kris2> mostly in the computer vision space. image captioning and stuff
14:39 < kris2> though i really love reading Deep RL paper haven't gotten my hands dirty with RL yet.
14:39 < kris2> What about you?
14:41 < lozhnikov> I am a postgraduate student at the moscow state university. Right now I work on computer modelling of Navier-Stokes equations, gasdynamics equations and so on
14:41 < lozhnikov> ML is my hobby
14:42 < kris2> oh that's really cool.
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--- Log closed Thu May 11 00:00:01 2017