it's not a white paper, it's a full scientific publication.
Literally the third line of the article says: "(Preliminary White Paper, November 9, 2015)"
TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
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Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#12Not to sound overly critical, but I don't enjoy the tone of this paper: presenting the concepts (e.g. graph operations) and features (e.g. automatic differentiation) of TensorFlow as new and novel when in fact very similar systems like Theano have existed since at least 2010 (which they finally get round to mentioning 14 pages in). They really should have shorn the paper in half and just focused on the distributed bi…
Where does it say that its automatic differentiation feature is new and novel? It actually calls out Theano in the paper: "Like Theano and Chainer, TensorFlow supports symbolic differentiation, thus making it easier to define and work with gradient based optimization algorithms." EDIT: Okay, I see that you already saw the reference. I guess I didn't find the tone as bad as you did. :-/
It's confusing but automatic differentiation, symbolic differentiation, and finite differences are all different approaches. "Automatic differentiation" typically refers to reverse order chain rule. Higher level libraries do really neat stuff with it. For example autograd can differentiate much of your numpy calls, even of composed of many complex functions.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#13it's not a white paper, it's a full scientific publication.
Is it peer reviewed?
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#14>> "A computation expressed using TensorFlow can be executed with little or no change on a wide variety of hetero- geneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards."
Are there other comparable systems that deploy using the same build across all platforms?
If not, putting aside other advantages like doing computation across multiple machines, being able to using the same build is a major step forward in my opinion.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#15Are there APIs to do the training in C++? I can't get away with doing what I want to do in the cloud (nor do I really want to).
Edit: I see http://mlpack.org/ - anyone have experience using this?
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#16it's not a white paper, it's a full scientific publication.
> it's not a white paper, it's a full scientific publication.
I believe the parent referring to the common usage of "white paper" to refer to B2B marketing materials that are not often written in an academic style.
I was pleasantly surprised to find this was a self-published scientific article and not what I and dekhn usually think of when we hear white-paper.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#17it's not a white paper, it's a full scientific publication.
It is not a full publication yet. The experiment section is left as to-do and that is a very important section in any paper.
They will most likely publish it in the near future.
Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#18Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#19Re: TensorFlow: Large-Scale Machine Learning on Distributed Systems (2015) [pdf]
#20it's not a white paper, it's a full scientific publication.
It is not a full publication yet. The experiment section is left as to-do and that is a very important section in any paper. They will most likely publish it in the near future.
Do you mean this:
http://arxiv.org/pdf/1603.04467v2.pdf