ajtulloch

15 ms·

91.
by ajtulloch 12y ago
FWIW, FFT-based convolutional methods aren't better for a lot of common sizes (including a bunch of AlexNet/VGG/GoogLeNet sizes). http://arxiv.org/abs/1412.7580 has a pretty thorough evaluation of im2co
92.
by ajtulloch 12y ago
(Author here) - the interactive workflow at http://nbviewer.ipython.org/gist/anonymous/115cc761db1a0d290... might be interesting, FWIW.
93.
by ajtulloch 12y ago
Hi folks, author here - thanks for the interest in the project. It's literally ~1,000 lines of Haskell that I wrote a couple of weekends ago (and don't use at all in production), but LMK if you find it useful in any way (or have f
94.
by ajtulloch 12y ago
With folly::gen using namespace folly::gen; auto total = seq(1) | mapped([](auto i) { return i * i; }) | take(10) | sum;
95.
by ajtulloch 12y ago
Facebook's folly::gen library (some examples at [1]) is another approach at this idea. It also has some nice features like buffered file reading, fork-join parallelism, etc. [1]: https://github.com/facebook/folly&
96.
by ajtulloch 12y ago
More precisely, it's an MLP library. My wishlist for Rust in scientific programming: - A really nice ndarray library interfacing with BLAS/LAPACK, like NumPy - A nice CUDA library for wrapping the C API - A Jupyter/IPython ke
97.
by ajtulloch 12y ago
See https://github.com/facebook/fbpca
98.
Show HN: A Haskell DSL for deep learning models(github.com)
10 points by ajtulloch 12y ago|0 comments
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by ajtulloch 12y ago
I don't understand the claim that you should get a large RAM GPU if you have a large dataset. It's the model and your training procedure that dictates the amount of GPU ram consumed (e.g. fft convolution kernels like fbcunn use a
100.
by ajtulloch 12y ago
I'm sorry - I love Haskell, and it's a great choice for a lot of domains, but it's a poor choice for numeric/scientific computation. The ecosystem just isn't there yet. Here's a recent r/haskell thread dis
101.
by ajtulloch 12y ago
Deep learning models definitely don't need an "absurdly vast amount of neurons" - for example, GoogLeNet (arguably the state of the art image classification model) has only ~6M parameters.
102.
by ajtulloch 12y ago
http://arxiv.org/pdf/1412.7580v2.pdf compares the convolutional implementation with the cuDNN layers. For the FC layers, it's just CuBLAS `sgemm`.
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by ajtulloch 12y ago
This paper describes one part of the fbcunn release (the fast convolution layers implemented via FFT, with the source available at https://github.com/facebook/fbcunn/tree/master/src/cuda/fft ).
104.
Facebook open sourcing AI research tools(mobile.nytimes.com)
4 points by ajtulloch 12y ago|0 comments
105.
by ajtulloch 12y ago
> You'd need to make your case in terms of things that science PIs find valuable, and in the process of trying to do that, you might actually discover that what you wanted to argue for isn't so critical after all. This is a gre
106.
by ajtulloch 12y ago
If you're looking for something more fully featured, https://code.google.com/p/google-glog/ is a great C++ logging library.
107.
by ajtulloch 12y ago
Facebook AI Research uses Torch heavily, and contributes improvements back to the open-source version.
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MonkeyBrains Raising Funding for Gigabit Wireless in SF(indiegogo.com)
6 points by ajtulloch 12y ago|0 comments
109.
by ajtulloch 12y ago
Doesn't `alloca` do exactly what you want? http://man7.org/linux/man-pages/man3/alloca.3.html
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by ajtulloch 12y ago
The GloVe vs word2vec is not as clear-cut as that - see https://plus.google.com/114479713299850783539/posts/BYvhAbgG... , https://docs.google.com/document/d/1ydIujJ7ETSZ688RGfU5IMJJs... f
111.
by ajtulloch 12y ago
How is this on the front page? This is a completely incoherent. For anyone actually interested in some interesting techniques for multi-GPU DNN training, http://arxiv.org/pdf/1404.5997v2.pdf and references therein are
112.
by ajtulloch 12y ago
I've been a convert to Tufte-LaTeX for a while - it produces beautifully formatted documents. I wrote a post a while ago on setting it (from a vanilla LaTeX setup) at http://ajtulloch.github.io/2012/getting-started
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by ajtulloch 12y ago
This is a great piece of work - thanks @karpathy.
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by ajtulloch 12y ago
Caffe (and other frameworks) provide exactly this. It's basically: 1) To setup, load a pre-trained AlexNet/Overfeat/other architecture model (e.g. trained on ILSVRC2012) 2) To get a vector from an image, run a forward pass o
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by ajtulloch 12y ago
FWIW, with folly::gen [1], this is just: using namespace folly::gen; byLine('/a/file/with/numbers') | eachTo<int> | sum; https://github.com/facebook/folly/tree/
116.
by ajtulloch 12y ago
"Shipping Out" [1] is another wonderful DFW essay, describing a week spent on a cruise ship in the Caribbean. [1]: http://harpers.org/wp-content/uploads/2008/09/HarpersMagazin...
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by ajtulloch 12y ago
Well said - this is a crazily over-engineered and highly problematic approach to the problem at hand.
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by ajtulloch 12y ago
If you want to use these in Torch7, my excellent colleague @soumith has released bindings at https://github.com/soumith/cudnn.torch . There are some benchmarks for the convolutional layers of a classic AlexNet model at
119.
by ajtulloch 12y ago
Note that this seems to be a grab-bag of papers/posters referencing "deep learning" from 2014. I'm not sure what this adds over the equivalent Google Scholar query. If you're interested in very recent advances in m
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Facebook’s Quest to Build an Artificial Brain Depends on Yann LeCun(wired.com)
44 points by ajtulloch 12y ago|9 comments

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