rcurtin_irc changed the topic of #mlpack to: mlpack: a scalable machine learning library (https://www.mlpack.org/) -- channel logs: https://libera.irclog.whitequark.org/mlpack -- NOTE: messages sent here might not be seen by bridged users on matrix, gitter, or slack
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<Aakash-kaushikAa> Hey @marcusedel:matrix.org, @kartikdutt18 I won't ne able to make it to today's meet, can we reschedule/cancel it?
<zoq[m]> Aakash-kaushikAa: Sure.
<kartikdutt18kart> Sure
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<zoq[m]> jonpsyjjb say4n I can't join the meeting today, but I think you got it covered; I'll read the updated gdoc.
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<say4n[m]> jjb: will you be joining us?
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<jonpsy[m]> zoq: The blog post you were saying, was about the summary of the entire GSoC write?
<jonpsy[m]> * zoq: The blog post you were saying, was about the summary of the entire GSoC right?
<zoq[m]> Yes, kartikdutt18 (kartikdutt18) created a repo for his final report in 2020 - https://github.com/kartikdutt18/GSoC-Work-Report
<zoq[m]> Which is super comprehensive and has all the information I would look for if I'm interested to read more about a certain project.
<zoq[m]> Having it on medium is fine as well, we just have to make sure it's accessible by everyone.
<jonpsy[m]> So, basically updating [this](https://github.com/jonpsy/GSoC-2021-mlpack) right?
<zoq[m]> Also, nothing set in stone, like in case of Aakash-kaushik (Aakash kaushik) the project is finished, but there is one remaining bug that we have to solve, before we can merge the PR, so in case we merge it after GSoC ended we can go ahead and update the report.
<zoq[m]> jonpsy[m]: Exactly, that is perfect.
<jonpsy[m]> @zoq say4n Another supplementary meeting on Thursday sounds good to you?
<jonpsy[m]> btw say4n remember the difficulties we had reading the code?
<say4n[m]> jonpsy[m]: I can make time, sure.
<say4n[m]> jonpsy[m]: Yes, what about it?
<jonpsy[m]> i think we've been putting too much emphasis on the code
<jonpsy[m]> rather than the paper itself
<say4n[m]> :p
<jonpsy[m]> lol, so i mean for ex: the hq thing?
<jonpsy[m]> its basically meant to take argmax over current actions, and preference vectors right?
<jonpsy[m]> why are we complicating things with all the reshape
<jonpsy[m]> hq is only used with single state in actual EQL code,
<jonpsy[m]> so batch_size is always 1
<say4n[m]> Aaah!
<jonpsy[m]> and soo many of the codes were re-shaped, re-viewed for the sole reason of ```torch.bmm```
<say4n[m]> so we can just handle the preference vector; one instance at a time
<jonpsy[m]> yeah! the code was purposefully written to be more general
<say4n[m]> GPUs :)
<jonpsy[m]> Yes!
<jonpsy[m]> they've done so many ```torch.expand, torch.scatter``` and all those
<say4n[m]> 😛
<jonpsy[m]> as if memory grows on trees!
<say4n[m]> :)
<jonpsy[m]> the point of my rant is, our basis of implementation should be the paper
<jonpsy[m]> and ive wasted so much of time not realising that
<say4n[m]> it’s good to look at reference implementations too, I think it helps with design choices
<jonpsy[m]> true, this is why i said the basis should be paper. We can check the code just for cross-reference, not as something to emulate
<say4n[m]> 💯
<zoq[m]> <jonpsy[m]> "@zoq say4n Another supplementar" <- Sounds good.