Re: [Scikit-learn-general] GSoC2015 topics

Thanks for trying to make some time :) On 03/06/2015 03:42 AM, Arnaud Joly wrote: Hi, Sadly this year, I won’t have time for mentoring. However, I will try to find some spare time for reviewing! Best regards, Arnaud On 05 Mar 2015, at 22:43, Andreas Mueller > wrote

Re: [Scikit-learn-general] GSoC2015 topics

Hi, Sadly this year, I won’t have time for mentoring. However, I will try to find some spare time for reviewing! Best regards, Arnaud > On 05 Mar 2015, at 22:43, Andreas Mueller wrote: > > Hi Wei Xue. > Thanks for your interest. > For the GMM project being familiar with DPGMM and VB should b

Re: [Scikit-learn-general] GSoC2015 topics

Hi Wei Xue. Thanks for your interest. For the GMM project being familiar with DPGMM and VB should be enough. We don't want to use Gibbs sampling in the DP. If you feel comfortable implementing a given derivation and have some understanding, that should be fine. For hyper-parameter optimization,

Re: [Scikit-learn-general] GSoC2015 topics

Hi, all I am a graduate student studying machine learning, and will probably apply GSOC project this year. I just took a loot at the wiki, and found two interesting topics for me. - Improve GMM - Global optimization based Hyper-parameter optimization For the GMM topic, I studied DP years a

Re: [Scikit-learn-general] GSoC2015 topics

Thanks for volunteering to assist, I updated the wiki accordingly :) On 03/05/2015 01:21 PM, Michael Eickenberg wrote: I unfortunately cannot lead any gsoc project this year, but can help out with code review and mentoring if sb else takes the lead. The two projects I can be of use for are CCA

Re: [Scikit-learn-general] GSoC2015 topics

I unfortunately cannot lead any gsoc project this year, but can help out with code review and mentoring if sb else takes the lead. The two projects I can be of use for are CCA/PLS rethinking and additive models. Michael On Thursday, March 5, 2015, Andreas Mueller wrote: > Can all would-be mento

Re: [Scikit-learn-general] GSoC2015 topics

Can all would-be mentors please register on Melange? The list of possible mentors lists Arnaud, probably a C&P from last year. Arnaud, are you up for mentoring again? Otherwise I'll remove you from the list. Then we'd currently have Gaël Varoquaux (not sure if you have time?), Vlad Niculae, Oliv

Re: [Scikit-learn-general] GSoC2015 topics

Hi, We are under the PSF umbrella. Gaël On Thu, Mar 05, 2015 at 10:26:19AM -0500, Wei Xue wrote: > It seems the results of organization application have come out. I didn't find > scikit-learn in the list of accepted organizations on Google GSOC main page > nor > in the PSF GSOC page. Is it reje

Re: [Scikit-learn-general] GSoC2015 topics

It seems the results of organization application have come out. I didn't find scikit-learn in the list of accepted organizations on Google GSOC main page nor in the PSF GSOC page. Is it rejected? 2015-03-03 19:39 GMT-05:00 Raghav R V : > FYI I've deleted this page - > > https://github.com/scik

Re: [Scikit-learn-general] GSoC2015 topics

FYI I've deleted this page - https://github.com/scikit-learn/scikit-learn/wiki/A-list-of-topics-for-the-GSOC-2015 in favor of the recently updated https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-%28GSOC%29-2015 On Wed, Mar 4, 2015 at 5:48 AM, Gael Varoquaux wrote: > Why do

Re: [Scikit-learn-general] GSoC2015 topics

Why don't you edit the "ideas" to start hashing a project there. Your proposal sounds serious enough to get a good start. Cheers, Gaël On Tue, Mar 03, 2015 at 11:34:27AM -0500, Andy wrote: > On 03/03/2015 11:31 AM, Artem wrote: > There was a discussion on metric learning a while ago, and s

Re: [Scikit-learn-general] GSoC2015 topics

On 03/03/2015 11:31 AM, Artem wrote: There was a discussion on metric learning a while ago, and several people expressed interest to see (and contribute to) it in sklearn. But, it looks like that attempt di

Re: [Scikit-learn-general] GSoC2015 topics

There was a discussion on metric learning a while ago, and several people expressed interest to see (and contribute to) it in sklearn. But, it looks like that attempt didn't get anywhere. What about a project to

Re: [Scikit-learn-general] GSoC2015 topics

> > ​ > Online low-rank matrix completion : this is from last year and I'm not sure if it is still desirable / don't know the state of the PR > ​ You mean ​ ​this one ​? I picked it up

Re: [Scikit-learn-general] GSoC2015 topics

Yes, the evidence accumulation approach is quite simple yet popular, and can be implemented on current methods in sklearn. However, I have a doubt about the current implementation of the agglomerative hierarchical clustering. Although it could be implemented differently, I would need to get the fin

Re: [Scikit-learn-general] GSoC2015 topics

Hey Everybody. Here is my somewhat consolidated list of ideas with minor comments. If anything is missing, please let me know. Also, I don't think people who want to mentor spoke up yet. I'll remove all people listed on the wiki as they were copy and pasted from last year, and I'd rather have ac

Re: [Scikit-learn-general] GSoC2015 topics

the paper is quite well cited (500): http://scholar.google.com/scholar?q=Combining%20multiple%20clusterings%20using%20evidence%20accumulation&btnG=Search&as_sdt=8001&as_sdtp=on I thought the idea was to add (some of) the ensemble methods described in the paper, which are meta-algorithms

Re: [Scikit-learn-general] GSoC2015 topics

Hi >> - data independent cv iterators ( https://github.com/scikit-learn/scikit-learn/ >> issues/2904) > >I think that that one is almost done, and it mostly needs someone to pick >it up (including reviewing it) and finish it. For me, this PR is very >important, as it is one of the blockers for 1.0

Re: [Scikit-learn-general] GSoC2015 topics

is there clear use for this clustering method and a sizable number of citations and obviously performance/accuracy/something benefits that warrant the time & maitenance cost then? Also by that @andreas, I'm not seeing a list of clustering methods to be added, right now it seems unbounded - i don't

Re: [Scikit-learn-general] GSoC2015 topics

On Tue, Feb 17, 2015 at 04:42:11PM -0800, Andy wrote: > On 02/13/2015 07:08 AM, Ronnie Ghose wrote: > > -1 we would have to build in support for more clustering methods > > ,sounds like a not-very-standalone proj > Why? We already have a bunch, right? I agree with Andreas that any addition should

Re: [Scikit-learn-general] GSoC2015 topics

On 02/13/2015 07:08 AM, Ronnie Ghose wrote: > -1 we would have to build in support for more clustering methods > ,sounds like a not-very-standalone proj Why? We already have a bunch, right? -- Download BIRT iHub F-Type

Re: [Scikit-learn-general] GSoC2015 topics

Hi Jan. That sounds great! Please share early version :) [not that I'd have time to review them :-/] I think breaking backward-compatibility will be necessary, and we should think of how we should go about that. Cheers, Andy On 02/13/2015 12:34 AM, Jan Hendrik Metzen wrote: > Having Bayesian op

Re: [Scikit-learn-general] GSoC2015 topics

Hi, Michael, thank you for your comments and ideas. I was probably not clear enough about the complexity to implement these algorithms, because I don't think it's a substantial amount of work. Excuse me if this was already understood, but let me add that consensus clustering is not limited to grap

Re: [Scikit-learn-general] GSoC2015 topics

Milton, my opinion is that the best work available in Python for clustering and community detection has been done in the igraph project ( http://igraph.org/). While I would personally love to see better support for these un- and semi-supervised taks in sklearn, it is a substantial investment of

Re: [Scikit-learn-general] GSoC2015 topics

-1 we would have to build in support for more clustering methods ,sounds like a not-very-standalone proj On Fri, Feb 13, 2015 at 10:02 AM, Milton Pividori wrote: > Hi, Andy. Thank you for the interest. > > Consensus clustering is usually used in the same context as traditional > clustering techn

Re: [Scikit-learn-general] GSoC2015 topics

Hi, Andy. Thank you for the interest. Consensus clustering is usually used in the same context as traditional clustering techniques. Many papers have reported significantly accuracy improvements when using these methods, as they can combine partitions from several different algorithm, finding inte

Re: [Scikit-learn-general] GSoC2015 topics

> What do you think about adding data visualization module to scikit-learn? No. This is outside of the scope of scikit-learn. Separating projects by scope is a good idea for many reasons. Gaël -- Dive into the World of P

Re: [Scikit-learn-general] GSoC2015 topics

, but quite sure there a lot of interesting and useful methods. > Date: Fri, 13 Feb 2015 09:34:30 +0100 > From: j...@informatik.uni-bremen.de > To: scikit-learn-general@lists.sourceforge.net > Subject: Re: [Scikit-learn-general] GSoC2015 topics > > Having Bayesian optimization in

Re: [Scikit-learn-general] GSoC2015 topics

Having Bayesian optimization in sklearn would be great +1 I was working recently on a sklearn-compatible rewrite of Gaussian processes. Main features are gradient-based hyperparameter optimization, kernel engineering and Gaussian process classification. The downside is that it is not completel

Re: [Scikit-learn-general] GSoC2015 topics

On Thu, Feb 12, 2015 at 06:33:42PM +0900, Mathieu Blondel wrote: > A grid-search related project could be useful: I think that the grid-search code is too convoluted and far reaching, and I think that it would be really hard for someone who does not already know scikit-learn well to pick up a proj

Re: [Scikit-learn-general] GSoC2015 topics

There are a lot of ways to speed them up as potential work, but the interface (and backend code) should be very stable first. Gradient based, latent variable approximation, low-rank updating, and distributed GP (new paper from a few weeks ago) are all possible, but would need to be compared to a ve

Re: [Scikit-learn-general] GSoC2015 topics

Sorry, I was using a possibly confusing idiom. The problem with our GP is not so much speed as interface and flexibility. Also, we are not using gradient based parameter optimization. On 02/12/2015 05:48 PM, Artem wrote: Do you have any particular ideas on how one could speedup GPs, besides rei

Re: [Scikit-learn-general] GSoC2015 topics

Do you have any particular ideas on how one could speedup GPs, besides reimplementing it in Cython? Looks like spearmint is completely pythonic, so they either as slow (or slower), or use different algorithm (I'm not very familiar with approaches to GPs). On Fri, Feb 13, 2015 at 12:41 AM, Andy wr

Re: [Scikit-learn-general] GSoC2015 topics

my short list is: GMM GP PLS/CCA so consolidate what we have. Alex -- Dive into the World of Parallel Programming. The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hu

Re: [Scikit-learn-general] GSoC2015 topics

Hi Milton. In which context is consensus clustering usually used, and what are the main applications? We will not add an external dependency, sorry. Cheers, Andy On 02/12/2015 01:55 PM, Milton Pividori wrote: Hi, guys. My name is Milton Pividori and this is the first time I write to this li

Re: [Scikit-learn-general] GSoC2015 topics

On 02/12/2015 04:33 AM, Mathieu Blondel wrote: > A grid-search related project could be useful: > > - multiple metric support (e.g., find the best model w.r.t. f1 score > and the best model w.r.t. AUC) > - data independent cv iterators > (https://github.com/scikit-learn/scikit-learn/issues/2904)

Re: [Scikit-learn-general] GSoC2015 topics

On 02/12/2015 02:09 AM, Michael Eickenberg wrote: On Thursday, February 12, 2015, Kyle Kastner > wrote: GSoC wise it might also be good to look at CCA, PLS etc. for cleanup. +1 We don't have a mentor, do we? --

Re: [Scikit-learn-general] GSoC2015 topics

On 02/12/2015 04:47 AM, Artem wrote: There are several packages (spearmint, hyperopt, MOE) offering Bayesian Optimization to the problem of choosing hyperparameters. Wouldn't it be nice to add such *Search[CV] to sklearn? Yes. I haven't really looked much into the spearmint approach, but befor

Re: [Scikit-learn-general] GSoC2015 topics

Something that hasn't been discussed in a while is semi-supervised learning. Issue #1243 suggests a generic meta-estimator approach may be feasible, but there might be a few different approaches available. More of an issue in my opinion is

Re: [Scikit-learn-general] GSoC2015 topics

Hi, guys. My name is Milton Pividori and this is the first time I write to this list. I'm a PhD student, working on clustering, particularly on consensus clustering. I'm relatively new to Python, and I am migrating legacy code from MATLAB. I plan to use scikit-learn as well as other libraries. Aft

Re: [Scikit-learn-general] GSoC2015 topics

What about adding multiclass support for the SVC "roc_auc" for grid search CV to the to do list? Best, Sebastian > On Feb 12, 2015, at 10:12 AM, Ronnie Ghose wrote: > > +1 to partial fit -1 to gam and more probabilistic things in sklean > > >> On Thu, Feb 12, 2015, 9:22 AM ragv ragv wrote:

Re: [Scikit-learn-general] GSoC2015 topics

+1 to partial fit -1 to gam and more probabilistic things in sklean On Thu, Feb 12, 2015, 9:22 AM ragv ragv wrote: > Hi, > > Is there a good deal of interest in having GAMs implemented? > > The timeline for such a project would go something like : > > Before GSoC: > * Implement SpAM > > Before M

Re: [Scikit-learn-general] GSoC2015 topics

Hi, Is there a good deal of interest in having GAMs implemented? The timeline for such a project would go something like : Before GSoC: * Implement SpAM Before Midterm : * Help merge pyearth into scikit learn * Implement Additive Model -> `AdditiveClassifier` / `AdditiveRegressor` ( Not sure if

Re: [Scikit-learn-general] GSoC2015 topics

On Thu, Feb 12, 2015 at 04:02:12AM -0700, Anirudh Acharya wrote: > But there is a pull request implementing the online non MCMC Latent > Dirichlet Allocation algorithm. > If not MCMC, could we try other approximate inference techniques like > variational bayes, which are comparatively fast

Re: [Scikit-learn-general] GSoC2015 topics

On 11 February 2015 at 23:21, Gael Varoquaux wrote: > On Wed, Feb 11, 2015 at 03:55:12PM -0700, Anirudh Acharya wrote: > > Is the following a good idea for GSoC 2015. > > > * Latent Dirichlet Allocation using Markov Chain Monte Carlo > > * Extend to do inference with online stream of documents. >

Re: [Scikit-learn-general] GSoC2015 topics

There are several packages (spearmint, hyperopt, MOE) offering Bayesian Optimization to the problem of choosing hyperparameters. Wouldn't it be nice to add such *Search[CV] to sklearn? On Thu, Feb 12, 2015 at 12:33 PM, Mathieu Blondel wrote: > A grid-search related project could be useful: > > -

Re: [Scikit-learn-general] GSoC2015 topics

A grid-search related project could be useful: - multiple metric support (e.g., find the best model w.r.t. f1 score and the best model w.r.t. AUC) - data independent cv iterators ( https://github.com/scikit-learn/scikit-learn/issues/2904) - anything else? Mathieu On Thu, Feb 12, 2015 at 5:53 PM,

Re: [Scikit-learn-general] GSoC2015 topics

> How about adding partial_fit to existing low rank methods or new incremental > algorithms? I think that making scikit-learn scale better is an important alley for the future. Thus I would personnally see very well any kind of efforts in this direction. However, these need to be well technically

Re: [Scikit-learn-general] GSoC2015 topics

+1 on the CCA / PLS refactoring, but this would require a student who is already well versed on these subjects. Mentoring could be an issue as well. Mathieu On Thu, Feb 12, 2015 at 4:14 PM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > On Thu, Feb 12, 2015 at 02:10:11AM -0500, Ronnie

Re: [Scikit-learn-general] GSoC2015 topics

How about adding partial_fit to existing low rank methods or new incremental algorithms? On Thu, Feb 12, 2015 at 12:44 PM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > On Thu, Feb 12, 2015 at 02:10:11AM -0500, Ronnie Ghose wrote: > > Do you mean refactoring? .. Are refactors/cleanups

Re: [Scikit-learn-general] GSoC2015 topics

On Thu, Feb 12, 2015 at 02:10:11AM -0500, Ronnie Ghose wrote: > Do you mean refactoring? .. Are refactors/cleanups rather than new features in > scope for GSOC project? Yes, if they are used to build new features on top of the refactor. But it is fine to alocate a significant amount of time to the

Re: [Scikit-learn-general] GSoC2015 topics

Do you mean refactoring? .. Are refactors/cleanups rather than new features in scope for GSOC project? On Thu, Feb 12, 2015 at 2:03 AM, Kyle Kastner wrote: > GSoC wise it might also be good to look at CCA, PLS etc. for cleanup. > On Feb 12, 2015 2:02 AM, "Kyle Kastner" wrote: > >> Plugin vs sep

Re: [Scikit-learn-general] GSoC2015 topics

On Thursday, February 12, 2015, Kyle Kastner wrote: > GSoC wise it might also be good to look at CCA, PLS etc. for cleanup. > +1 achieving that in satisfactory way may be non trivial but would be very useful. Adding speedups for the nsamples < nfeatures case would render this tool usable for wh

Re: [Scikit-learn-general] GSoC2015 topics

GSoC wise it might also be good to look at CCA, PLS etc. for cleanup. On Feb 12, 2015 2:02 AM, "Kyle Kastner" wrote: > Plugin vs separate package: > libsvm/liblinear are plugins whereas "friend" libraries like lightning are > packages right? > > By that definition I agree with Gael - standalone p

Re: [Scikit-learn-general] GSoC2015 topics

Plugin vs separate package: libsvm/liblinear are plugins whereas "friend" libraries like lightning are packages right? By that definition I agree with Gael - standalone packages are best for that stuff. I don't really know what a "plugin" for sklearn would be exactly. On Feb 12, 2015 1:58 AM, "Gae

Re: [Scikit-learn-general] GSoC2015 topics

yup so tl;dr no gpu things in the GSOC On Thu, Feb 12, 2015 at 1:58 AM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > > no i mean external plugin that they have to support - we're hands off. > > we can link to it but that's it - no other guarantees like we've done > > in the past iirc

Re: [Scikit-learn-general] GSoC2015 topics

> no i mean external plugin that they have to support - we're hands off. > we can link to it but that's it - no other guarantees like we've done > in the past iirc That doesn't work well: if it has our name on it, people still associate it to us, and land on our tracker, or complain that scikit-le

Re: [Scikit-learn-general] GSoC2015 topics

Ah yeah, in that case I think it would be OK. At least that is the path we took for sklearn-theano. It's not really a GSoC thing but some implementation of common algs in numba could be interesting in general to compare with cython versions. On Feb 12, 2015 1:52 AM, "Ronnie Ghose" wrote: > no i

Re: [Scikit-learn-general] GSoC2015 topics

> Even having a separate plugin will require a lot of maintenance. I am > -1 on any gpu stuff being included directly in sklearn. Maintenance for > sklearn is already tough, and trying to support a huge amount of custom > compute hardware is really, really hard. Ensuring numerical stability > betwe

Re: [Scikit-learn-general] GSoC2015 topics

As for MCMC PyMC (2 and 3) are both great for it. emcee is really cool too. I don't see a huge reason to rehash that, and most models which need MCMC during training bear a pretty high computational cost. Unless the accuracy gain for some algorithm is enormous it seems like it's best to let other

Re: [Scikit-learn-general] GSoC2015 topics

no i mean external plugin that they have to support - we're hands off. we can link to it but that's it - no other guarantees like we've done in the past iirc On Thu, Feb 12, 2015 at 1:48 AM, Kyle Kastner wrote: > Even having a separate plugin will require a lot of maintenance. I am -1 > on any g

Re: [Scikit-learn-general] GSoC2015 topics

Even having a separate plugin will require a lot of maintenance. I am -1 on any gpu stuff being included directly in sklearn. Maintenance for sklearn is already tough, and trying to support a huge amount of custom compute hardware is really, really hard. Ensuring numerical stability between OS/BLAS

Re: [Scikit-learn-general] GSoC2015 topics

Also, isn't MCMC implemented in PyMC quite effectively? I haven't been following the development of that codebase, but last time I checked it seemed like there were a lot of interesting things you could do with it re: probabilistic models On Wed, Feb 11, 2015 at 10:39 PM, Chris Holdgraf wrote: >

Re: [Scikit-learn-general] GSoC2015 topics

can we have gpu-based/dependent algos as a separate plugin like has been done for other things? adding more dependencies sounds irksome. On Thu, Feb 12, 2015 at 1:24 AM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > > I think I mentioned GaussianRBM as a good addition before, so I will

Re: [Scikit-learn-general] GSoC2015 topics

> I think I mentioned GaussianRBM as a good addition before, so I will mention > that again here. RBMs and Autoencoders are still useful for feature extraction > in some cases, so it seems reasonable to have them around. I have seen any convincing demonstration using our Bernouilli RBMs. It doesn'

Re: [Scikit-learn-general] GSoC2015 topics

On Wed, Feb 11, 2015 at 03:55:12PM -0700, Anirudh Acharya wrote: > Is the following a good idea for GSoC 2015. > * Latent Dirichlet Allocation using Markov Chain Monte Carlo > * Extend to do inference with online stream of documents. MCMC no. We ruled against it, as MCMC require techniques that a

Re: [Scikit-learn-general] GSoC2015 topics

http://scikit-learn.org/dev/faq.html#will-you-add-gpu-support On 02/11/2015 05:59 PM, Artem wrote: There was an interview with Ilya Sutskever about deep learning (http://yyue.blogspot.ru/2015/01/a-brief-overview-of-deep-learning.html), where he states that DL's success can be attributed to 3 ma

Re: [Scikit-learn-general] GSoC2015 topics

On 02/11/2015 06:07 PM, Kyle Kastner wrote: > pylearn2 is not even close to sklearn compatible. Small scale > recurrent nets are in PyBrain, but I really think that any seriously > usable neural net type learners are sort of outside the scope of > sklearn. Others might have different opinions,

Re: [Scikit-learn-general] GSoC2015 topics

pylearn2 is not even close to sklearn compatible. Small scale recurrent nets are in PyBrain, but I really think that any seriously usable neural net type learners are sort of outside the scope of sklearn. Others might have different opinions, but this is one of the reasons Michael and I started skl

Re: [Scikit-learn-general] GSoC2015 topics

But for cnns as you mention - gpusss On Wed, Feb 11, 2015, 5:59 PM Artem wrote: > There was an interview with Ilya Sutskever about deep learning ( > http://yyue.blogspot.ru/2015/01/a-brief-overview-of-deep-learning.html), > where he states that DL's success can be attributed to 3 main breakthrou

Re: [Scikit-learn-general] GSoC2015 topics

There was an interview with Ilya Sutskever about deep learning ( http://yyue.blogspot.ru/2015/01/a-brief-overview-of-deep-learning.html), where he states that DL's success can be attributed to 3 main breakthroughs: 1. Computing resources. 2. Large datasets. 3. Tricks of the trade, discovered in re

Re: [Scikit-learn-general] GSoC2015 topics

Is the following a good idea for GSoC 2015. * Latent Dirichlet Allocation using Markov Chain Monte Carlo * Extend to do inference with online stream of documents. Anirudh On 11 February 2015 at 15:32, Andy wrote: > The MLP is pretty close to being merged, I think the autoencoder, too. > We

Re: [Scikit-learn-general] GSoC2015 topics

The MLP is pretty close to being merged, I think the autoencoder, too. We don't want to rely on Theano, and there is already pylearn2, which is a great library for deep learning in python. I'm not sure if pylearn2 is completely sklearn compatible, but I don't think there is any need to create an

Re: [Scikit-learn-general] GSoC2015 topics

as far as I know, sklearn has only an RBM module, but does not support multilayer perceptrons (MLPs), autoencoder, or recurrent neural networks. Are there any plans do extend sklearn by some neural network related modules? There was a GSoC project on neural networks last year (http://goo.gl/buH

Re: [Scikit-learn-general] GSoC2015 topics

I'd say this years JMLR is too fresh ;) On 02/09/2015 04:31 PM, Ronnie Ghose wrote: are we interested in more discriminant methods? There were a few more @ JMLR this year On Mon, Feb 9, 2015 at 4:28 PM, Alexandre Gramfort mailto:alexandre.gramf...@m4x.org>> wrote: please wait a bit so

Re: [Scikit-learn-general] GSoC2015 topics

In particular we need to update the mentors. Currently we have last years: ""Here are people that have said that they might be available for mentoring: Gaël Varoquaux, Vlad Niculae, Olivier Grisel, Andreas Mueller, Jason Rudy, Robert Layton, Alexandre Gramfort, Arnaud Joly, Jaidev Deshpande (ne

Re: [Scikit-learn-general] GSoC2015 topics

what do you have in mind? A -- Dive into the World of Parallel Programming. The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software develo

Re: [Scikit-learn-general] GSoC2015 topics

are we interested in more discriminant methods? There were a few more @ JMLR this year On Mon, Feb 9, 2015 at 4:28 PM, Alexandre Gramfort < alexandre.gramf...@m4x.org> wrote: > please wait a bit so we finalize the list. It's not definitive. > > A > > On Mon, Feb 9, 2015 at 6:39 PM, ragv ragv wro

Re: [Scikit-learn-general] GSoC2015 topics

please wait a bit so we finalize the list. It's not definitive. A On Mon, Feb 9, 2015 at 6:39 PM, ragv ragv wrote: > Hi, > > I saw implementing GAMs as one of the suggested topics for GSoC 2015. > Could I take that up? I saw your ( Alex's ) name under that. If yes, > please let me know I'll star

Re: [Scikit-learn-general] GSoC2015 topics

Hi, I saw implementing GAMs as one of the suggested topics for GSoC 2015. Could I take that up? I saw your ( Alex's ) name under that. If yes, please let me know I'll start working on the same and if you permit me to, I'll start a wiki page for my proposal and timeline. Thanks ragv -

Re: [Scikit-learn-general] GSoC2015 topics

FYI I created the wiki page but it needs editing. So it's WIP https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-(GSOC)-2015 A -- Dive into the World of Parallel Programming. The Go Parallel Website,

Re: [Scikit-learn-general] GSoC2015 topics

On Fri, Feb 6, 2015 at 3:22 AM, Joel Nothman wrote: > > I think adding partial_fit functions in general to as many algorithms > as possible would be nice > > Which could be a project in itself, for someone open to breadth rather > than depth. >

Re: [Scikit-learn-general] GSoC2015 topics

> I think adding partial_fit functions in general to as many algorithms as possible would be nice Which could be a project in itself, for someone open to breadth rather than depth. On 6 February 2015 at 06:43, Kyle Kastner wrote: > IncrementalPCA is done (have to add randomized SVD solver but t

Re: [Scikit-learn-general] GSoC2015 topics

IncrementalPCA is done (have to add randomized SVD solver but that should be simple), but I am sure there are other low rank methods which need a partial_fit . I think adding partial_fit functions in general to as many algorithms as possible would be nice Kyle On Thu, Feb 5, 2015 at 2:12 PM, Aksh

Re: [Scikit-learn-general] GSoC2015 topics

Is Online low rank factorisation still a vaild idea for this year? As it was in the last years idea list. On Thu, Feb 5, 2015 at 9:49 PM, Alexandre Gramfort < alexandre.gramf...@telecom-paristech.fr> wrote: > > I just looked at the list from last year, and what seems most relevant > > still is GM

Re: [Scikit-learn-general] GSoC2015 topics

> I just looked at the list from last year, and what seems most relevant > still is GMMs, > and possibly the coordinate descent solvers (Alex maybe you can say what > is left there or > if with the SAG we are happy now?) there is work coming in coordinate descent and SAG is almost done. I don't th

Re: [Scikit-learn-general] GSoC2015 topics

I have the same feeling. On Thu, Feb 05, 2015 at 03:56:12PM +, Thomas Johnson wrote: > So I don't really have a 'deep' understanding of deep learning, but aren't > things like Gaussian RBMs becoming obsolete? I thought I read that Hinton said > that the current state-of-the-art is Really Big n

Re: [Scikit-learn-general] GSoC2015 topics

So I don't really have a 'deep' understanding of deep learning, but aren't things like Gaussian RBMs becoming obsolete? I thought I read that Hinton said that the current state-of-the-art is Really Big networks that just use standard backprop (plus tricks like dropout). Is that not correct, or is H

Re: [Scikit-learn-general] GSoC2015 topics

I think most of the GP related work is deciding what the sklearn compatible interface should be :) specifically how to handle kernels and try to share with core codebase. The HODLR solver of George could be very nice for scalibility but algorithm is not easy. There are a few other options on that

Re: [Scikit-learn-general] GSoC2015 topics

With respect to Gaussian processes, there are some good packages in python already (https://github.com/SheffieldML/GPy, https://github.com/dfm/george, probably others). In particular, GPy does not require any other dependencies over and above those already required by sklearn. Maybe a reasonable

Re: [Scikit-learn-general] GSoC2015 topics

On 02/05/2015 01:03 PM, Daniel Sullivan wrote: > I'm still in the process of polishing up SAG, hopefully I can get > something commit-able soon Sure, no hurry. My question was more "Do we want anything more that is not covered by your work on SAG" ;) -

Re: [Scikit-learn-general] GSoC2015 topics

I'm still in the process of polishing up SAG, hopefully I can get something commit-able soon On Thu, Feb 5, 2015 at 12:52 PM, Andy wrote: > Hi Christof. > Good question. I don't think we came up with a list yet. > I just looked at the list from last year, and what seems most relevant > still is

Re: [Scikit-learn-general] GSoC2015 topics

Hi Christof. Good question. I don't think we came up with a list yet. I just looked at the list from last year, and what seems most relevant still is GMMs, and possibly the coordinate descent solvers (Alex maybe you can say what is left there or if with the SAG we are happy now?) There is still s