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Allocations don't have to be expensive if your GC is smart. Smart as C++ destructors positioning :D


> if your GC is smart

Can you say more? I don't know how C++ uses destructor positioning? Seems to me if it gets to GC it's too late, you already made an allocation which is the expensive part.


Yep, as you acknowledge, your reasoning is irrational. Replacing regular milk with coconut milk is more environmentally friendly, medium-chain fatty acids are better for the liver and heart.

Still, veganism is much more than just a diet and this meaning is what was meant by the vegan-friendly natural latex.

I do assume that people who have never googled "veganism" might have negative opinion of these condoms, but I guess they also won't be aware of the struggles of third world countries and won't care about the goal of this company.

Not that I don't find the fact that veganism had to be mention entertaining.


> Replacing regular milk with coconut milk is more environmentally friendly, medium-chain fatty acids are better for the liver and heart.

Except it doesn't taste as good, which is 90% of the whole point.


At the very least, it doesn't taste the same which is probably most of where the complaint comes from. 'Good' is entirely relative.


Except that milk is not just empty fat. The CLA in grass-fed milk, the vitamin D, and many other vitamins in milk are not present in coconut milk. Best is to put coconut oil in your warm milk!


The author could change the generation of piece by generating a short theme, then fixing that theme at particular points in the note sequence and then generate the gaps, and then repeat the thing as recursively as he wants.

This would provide some recurrence of melodies and would sound infinitely better. Problem with these kinds of models is the window based structure without the notion of the general theme. Theme should then be added by the human, and everything else can be filled using the model.


Why do they need a deep learning model for this? They are obviously targeting signs, product names, menus and similar. Model will obviously fail in translating large texts.

Was there any advantage of using a deep learning model instead of something more computationally simple?


http://musicbrainz.org/ already has this data available.


Yep. And I was participating many years ago when we figured out a pragmatic way to handle the mapping between simple ID3 style fields and the complex needs of classical - so that non-classical aware media players were at least usable. I wrote the first draft of this: https://wiki.musicbrainz.org/Style/Classical

but I haven't looked at Musicbrainz for many years to see how the rules and tools have changed.

However - no-one else since has even tried to tackle this problem.


https://www.youtube.com/watch?v=kW39Mt5kscQ

Another interesting alternative. Prettier than QR and IMO meshtag.


There's also a compiler for Haskell to JavaScript called GHCJS[0]. Many of Haskell libraries can be compiled easily. I think someone compiled Pandoc directly to JS and made a web interface and bundled the app[2].

One interesting things besides web that people have been doing with it is writing Atom editor plugins[1].

[0]: https://github.com/ghcjs/ghcjs

[1]: http://edsko.net/2015/02/14/atom-haskell/

[2]: http://markup.rocks/


This doesn't seem to solve the puzzle. As much as DSL processing part is done, the whole point of the puzzle was to make the operations sublinear in time complexity.


Any generative model can be used to generate a follow up chord. Of course, you need to learn it on a significant amount of data.

There has been some generative modelling of CNNs but I'm not sure if there's generative ability in any of the existing frameworks.


I'm really interested to see how the author will turn Cover Trees into a generic inference algorithm for bunch of models in HLearn library.


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