kkjkok

6 ms·

1.
by kkjkok 13y ago
The most interesting part of that is the WHO of spread spectrum... Hedy Lamarr (movie star) and George Antheil (composer). Very fascinating that two major Hollywood players were able to innovate in hard science/math. A different era. I
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by kkjkok 13y ago
To be fair, the FFTW/CUDA thing is due to fundamentally different hardware architectures which drove design constraints for these types of libraries. FFTW was never meant to run on a dedicated, ultra-parallel processor with highly opti
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by kkjkok 13y ago
I was (and still am) trying to use an already trained CIFAR10 net in a similar manner to DeCAF/ImageNet. Because CIFAR10 operates on 32x32 color images, I did the same thing for the input of the DeCAF experiment. As far as I know, the
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by kkjkok 13y ago
Check out this discussion - it may help you decide what card to get. There was also an email somewhere about how TITAN is currently not any faster than a 580, though no hard numbers. https://groups.google.com/forum/#!to
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by kkjkok 13y ago
From what I understand, it is due to the programming specifics of the training algorithms, primarily being focused on exploiting certain registers and architecture features specific to Fermi. The code actually got updated from GTX280 series
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by kkjkok 13y ago
Question: do you/will you plan to support converting GPU nets to CPU, perhaps by keeping weights and architecture definition separate from PyCUDA dependent structures during serialization? I have found that using a trained net for prep
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by kkjkok 13y ago
There is also DeCAF, which actually includes a way to load a pretrained ImageNet network based on cuda-convnet. I have had pretty recent success using this blob as preprocessing for image classification, ala http://arxiv.org&#x2F
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by kkjkok 13y ago
The convolutional neural network code that pylearn2 and the Toronto group use is specifically tuned for GTX580 cards - users have reported factors of 2x-10x slowdown using Kepler series cards. In general, most users (of pylearn2 at least) h
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by kkjkok 13y ago
Check out this paper: Practical recommendations for gradient-based training of deep architectures, Y. Bengio http://arxiv.org/abs/1206.5533 There is a section on weight initialization on page 15. In general, this paper
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by kkjkok 13y ago
Nope - the amount of numbers is in fact identical. The discrete Fourier transform has redundant information in bins above NFFT/2 (where NFFT is the size of the transform/number of samples in the signal) for purely real valued inpu