When claire stopped writing the tgif emails I was very sad, among others I talked to. When I heard, years later, about all this bs she went through, it helped crack the illusion for me. So sad.
The flip side is redistributive pensions require an ever growing population and most European pension systems will go bankrupt within a couple of decades given current birth and immigration rates.
The historic picture makes a little more sense (though this is not something a 5yo would understand).
We call these things embeddings because you start with a very high dimensional space (image a space with one dimension per word type, where each word is a unit vector in the appropriate dimension) and then approximate distances between sentences / documents / n-grams in this space using a space with much smaller dimensionality. So we "embed" the high dimensional space in a manifold in the lower dimensional space.
It turns out though that these low dimensional representations satisfy all sorts of properties that we like which is why embeddings are so popular.
I think Keras is a real deal framework. It provides a higher-level API than most other frameworks, but it has pretty sweet portability of models across frameworks and platforms and most research papers are implementable in Keras without too much trouble.
We're still fairly early in the project, so for now threading is the only supported way.
We can do better, however, and we're working on ways to leverage the hardware better (for example, if you have no data-dependent choices in your model we can enqueue kernels in parallel on all GPUs in your machine at once from a single python thread, which will perform much better than explicit python multithreading).
Stay on the lookout as we release new experimental APIs to leverage multiple GPUs and multiple machines.
Ah, I didn't know SavedModel didn't work in android. I think freezing is still the way to go there? I'm sorry, I don't personally work on the mobile side of things.
Did you try using SavedModel? It should be seamless to use downstream with tensorflow serving and it's not that hard to get estimators to spit those out.
I write tests not to convince myself my code is correct (it often is, and repl and ad-hoc testing are more than enough to make sure it does the right thing now) but to prevent myself & future others from breaking it as they maintain it in the future.
Regarding the perceptron, most modern texts also have a regret analysis for the perceptron, which coupled with an online-to-batch conversion tells you how well do you expect a perceptron to perform on unseen data after a single pass, and it's usually a very good estimate (the answer is on average about as well as it did on the examples in the training data).
ML PhD student here. The reason why this is different is that the parallel monte carlo simulations are running on different subsets of the data in each machine, and then averaged.
It is not obvious that this can work at all in some cases. Think, for example, a clustering model. If there are two clusters, but one machine calls them A B and the other machine calls them B A, averaging will give you useless results.
So the contribution of this paper is finding a set of models on which naive averaging works, and showing an efficient mapreduce implementation of it.
That said, I don't find the paper particularly interesting.
Fun fact: this is pretty much the same dynamic program as CKY, the parsing algorithm that can parse any context-free language in the appropriate normal form (only productions looking like A->BC or A->a) in time cubic on the length of the sentence.
The fallacy behind arguments like this is that when someone says that we're "proposing to push people to speak at conferences who aren't necessarily the most knowledgeable speakers" it is implicitly assumed that it is known how to rank the speakers, and that doing so is easy.
In practice, however, while one can often tell a really bad speaker and a really good speaker apart there are plenty of bordeline cases and the variance is big, so using minority-biased criteria to "break ties" will not necessarily lead to worse speakers on average, as long as these criteria are uncorrelated with actual skills.
(and I'm not sure they are, as the mean quality of a woman-presented talk in conferences I've attended has been consistently higher than the mean man-presented talk)
http://www.ic.unicamp.br/~tachard/