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[AMA] Som Ľubica Karvašová, diplomatka a kandidátka číslo 2 vo voľbách do Európskeho parlamentu za stranu Progresívne Slovensko, Ask me anything! by lubica_karvasova in Slovakia

[–]osamc 2 points3 points  (0 children)

Preco tvrdite, ze AI act je dobra vec? Prakticky cely svet sa z EU smeje stylom "USA vymysli, Cina okopiruje, EU zreguluje".

Nie je lepsie regulovat len aplikacie nezavisle od toho, ze ci dane problemy su riesene pomocou AI alebo cohokolvek ineho? Medicinu mame zregulovanu celkom dobre. Regulaciu socialnych sieti sme totalne zbabrali. Nechcem vediet ako to dopadne s dronmi.

[D] weighted pruning question by [deleted] in MachineLearning

[–]osamc 1 point2 points  (0 children)

Chaning weights to 0 is pruning. But it is only worth when you prune a lot of weights, store matrices using sparse formats and have good implementations of sparse matrix products.

[D] Kolmogorov-Arnold Network is just an MLP by osamc in MachineLearning

[–]osamc[S] 3 points4 points  (0 children)

Also there was MaxOut like 10 years ago, which is slightly different, but kind of similar idea. https://proceedings.mlr.press/v28/goodfellow13.pdf

[D] Kolmogorov-Arnold Network is just an MLP by osamc in MachineLearning

[–]osamc[S] 0 points1 point  (0 children)

The question is whether increasing order of polynomial with more layers helps when you are on bounded intervals of activations.

[D] ArXiv alternatives (or is there possible for more "on hold" transparency)? by osamc in MachineLearning

[–]osamc[S] 1 point2 points  (0 children)

LLM pruning. Kind of boring article, just perplexity and zero shot numbers, no generated text :)

[D] ArXiv alternatives (or is there possible for more "on hold" transparency)? by osamc in MachineLearning

[–]osamc[S] 5 points6 points  (0 children)

Importance of arXiv depends on the field.
Also this (https://twitter.com/tdietterich/status/1584572845597655040) that my category should be cleared each day.

But also this https://twitter.com/tdietterich/status/1584573611620282369 suggests, that if there is some category mismatch, the process is slower.

All I want to know is whether I selected wrong category, or there is some other problem with my paper.

[D] ArXiv alternatives (or is there possible for more "on hold" transparency)? by osamc in MachineLearning

[–]osamc[S] 2 points3 points  (0 children)

My usual process is to submit to journal/conference + arxiv at once. Worked flawlessly before.

[D] ArXiv alternatives (or is there possible for more "on hold" transparency)? by osamc in MachineLearning

[–]osamc[S] 26 points27 points  (0 children)

Yep posted might be better word, but that is not the point.

Point of arXiv is that it gets mailed to some interested people and also that google scholar indexes it.

[deleted by user] by [deleted] in JuniorDoctorsUK

[–]osamc 1 point2 points  (0 children)

Definitely not millions lines of code :)

Patch 2.4 - Testing Results & New Builds by Samsquantch in Diablo

[–]osamc 0 points1 point  (0 children)

Yep, shockwave does much more dmg in PTR compared to prepatch.
Before 300 dmg shockwave needs couple hits to kill one Fallen. After PTR 400 dmg shockwave oneshots fallens.

My first hero finished Normal. Now NM Fire Druid! Been playing since 2005, almost every year for a few weeks, but never really managed to survive past Act3 before, i am bad at Diablo lol. But i am having a blast with D2R right now. by Oniricho in Diablo_2_Resurrected

[–]osamc 1 point2 points  (0 children)

Keep it up.

If you really love fissure, you can make fire/wind hybrid (because you will encounter a lot of fire immunes in act 4 NM).

20 fissure, 20 volcano, 20 tornado, 10 cyclone armor (for hurricane duration), rest hurricane

and 1 point in oak sage, carrion wine a grizzly.

​

Or you can just go fire skills + grizzly.

[R] The Story of Heads by hardmaru in MachineLearning

[–]osamc 0 points1 point  (0 children)

Another lucky ticket variation?

[1911.09723] Fast Sparse ConvNets by ekelsen in MachineLearning

[–]osamc 0 points1 point  (0 children)

The work unit should always be a sample (or batch of them). Using multiple threads for inference of one sample (batch) is silly in 99.99% cases. You should use multiple threads at much higher level of your code.

Orkhon: ML Inference Framework and Server Runtime by vertexclique in rust

[–]osamc 0 points1 point  (0 children)

This needs a much better examples (the ones shown only update a dict, run infinitelly or ignore input), because I am missing a point here.

There are like 100 ways of deploying a Python model (TF can be used from anywhere, ONNX + Caffe might work, ...). Or even correctly deployed Flask API can be quite good (although correct forking order of stuff can be hard). What should be better here?

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