andreasvc

Why would we never know? Quoting from the blog post: "The specific reasons given were that [...]" followed by a bullet list. Your guess is unwarranted speculation.

Ah, I see. That really doesn't deserve to be called an idiom, it's a clever hack. But it's nice to know about it. It seems less ugly than the walrus operator to me, and it doesn't leak the variable outside of the comprehension.

I'm puzzled by this example:

    sums = [s for s in [0] for x in data for s in [s + x]]

Why would you do "for s in" twice? Is that intentional? It would make more sense to me if the variables would have been different. And why would you want to add 0 to numbers?! Curious about a real world use case for this.

What are the advantages of Re over Re2?

bollu, multiple people have shown that your claim that the paper doesn't match the code is flat out wrong. I think at this point you should issue a retraction of your wildly inappropriate suggestion of academic dishonesty.

It is an extremely thin font which makes it unsuitable for screen reading.

Why do you think English would be least compressible? Is that based on conjecture or have you investigated this? Why would artificial language be more compressible? That seems completely orthogonal to me (by definition, an artificial language can be designed with whatever properties you choose). Fortran may be more compressible due to its limited set of keywords, but it's my impression that Ithkuil is by design more information dense and thus harder to compress than English.

The most efficient language is the least compressible language only in a narrow and arbitrary sense of efficient. There are many considerations such as what is efficient for the speaker, the hearer, redundancy to noise, efficiency with respect to particular purposes, etc. We can assume that natural languages will generally make a good trade-off across these factors, and searching for the most efficient language in one particular narrow sense is not very useful. Moreover, compression of text focuses only on surface form, completely ignoring the dimension of meaning.

Yes, in the context of text classification a bag of words model will refer to that, or combined with some other linear model like linear SVM or naive bayes.

The queen - woman example is when you try to make a model of word semantics, such as with word2vec. In a document classification task the vectors represent documents.

You mention a single counterexample, but is it "definitely not true"? I think there is a strong publication bias: papers will report when they improve on the baseline, but that's likely not representative for the common case (e.g., limited data, no pretrained model available, no time for extensive parameter tuning).

The article states he's perfectionist. He probably wouldn't be OK with that.

Yup, that's a downside. The advantage is that it's much simpler and will also work when you're not on your home network.

Regarding that idea for a universal integer set, roaring bitmaps implement that.

Would love to read more about this. I've read that early programming was thought of like a glorified telephone operator, but that these women had advanced mathematics degrees.

I can't find anything non-biased though, everyone just wants to push the female victim narrative.