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True. The cases where this would be useful (extremely low training data) there are machine learning methods that are much much better than deep learning, which

True. The cases where this would be useful (extremely low training data) there are machine learning methods that are much much better than deep learning, which is a glutton for data comparatively. E.g. handwriting recognition or signature recognition (where you would have only a few samples). http://www.sciencemag.org/content/350/6266/1332.abstract http://www.sciencemag.org/content/350/6266/1332.abstract

IanCal 11y ago
I didn't get the impression this was for training, but for running a trained network. This may lead to high quality speech recognition or image recognition right on the device, without requiring a beast of a GPU.
- cscurmudgeon 11y ago
  Ah ok, got it. But is running that slow that you need special hardware?
  - IanCal 11y ago
    The large nets, yes. Partly because bigger nets tend to do better so we've built massive nets that run pretty well on big GPUs. But that's the state of things, we have impressive results but from huge neural nets that we really cannot run in realtime on a mobile device. It's particularly bad if you want to run them on a small, battery powered device. Far more so if you only have one image to process, here they're seeing single image speedups of over 10x compared to GPUs (but slower than batched processing) and energy efficiencies about 1000+ times better than mobile GPUs (and far more compared to the beasts in your desktop).
    - nl 11y ago
      But that's the state of things, we have impressive results but from huge neural nets that we really cannot run in realtime on a mobile device. This is absolutely incorrect. A mobile device can execute a pretrained model fine. See, for example [1][2][3]. Google's new TensorFlow NN system is explicitly designed to be able to run on mobile devices and comes with a pre-trained image classification NN that workd fine on mobile devices. This doesn't mean that energy saving is unimportant on a mobile device of course. But there are very widely deployed production systems (eg all of Android) that use them now with no special GPU acceleration. [1] http://googleresearch.blogspot.com.au/2015/07/how-google-translate-squeezes-deep.html http://googleresearch.blogspot.com.au/2015/07/how-google-tra... [2] http://googleresearch.blogspot.com.au/2015/09/google-voice-search-faster-and-more.html http://googleresearch.blogspot.com.au/2015/09/google-voice-s... [3] http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41176.pdf http://static.googleusercontent.com/media/research.google.co...
      - versteegen 11y ago
        Well, it depends on the neural network you want to use. At computer vision/image processing conferences I find that it is much more common for people to use preexisting DNNs and plug them into their systems rather than train their own (why would you waste weeks or months on that?). The good pretrained DNNs available today tend to be huge, requiring a large amount of video RAM. I've talked to people writing realtime image processing software (GPU acceleration required) intended for handheld devices which can't yet actually run on the device because they don't have a suitable DNN which will fit in VRAM.
        dharma1 11y ago
        mxnet has some optimised models for mobile devices - http://mxnet.readthedocs.org/en/latest/tutorial/smart_device.html http://mxnet.readthedocs.org/en/latest/tutorial/smart_device...
        nl 11y ago
        3.5% top-5 error rate on ImageNet running on Android. Hard to beat that (unless you are Microsoft Research Asia). https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android https://github.com/tensorflow/tensorflow/tree/master/tensorf...
      - IanCal 11y ago
        > A mobile device can execute a pretrained model fine. Of course it can. The problem is with the size of the network you might want to run. From your first link, which is about single character level image processing > We needed to develop a very small neural net, and put severe limits on how much we tried to teach it—in essence, put an upper bound on the density of information it handles. And from the voice training paper: > While our server-based model has 50M parameters (k = 4, nh = 2560, ni = 26 and no = 7969), to reduce the memory and computation requirement for the embedded model, we experimented with a variety of sizes and chose k = 6, nh = 512, ni = 16 and no = 2000, or 2.7M parameters AlexNet is, what, 60M+? VGG is pretty big too. I guess I wasn't too clear. Yes, there are good results from nets that we can run on mobile devices in realtime. We do, however, want to run significantly larger nets.

IanCal 11y ago

I didn't get the impression this was for training, but for running a trained network. This may lead to high quality speech recognition or image recognition right on the device, without requiring a beast of a GPU.

cscurmudgeon 11y ago
Ah ok, got it. But is running that slow that you need special hardware?
- IanCal 11y ago
  The large nets, yes. Partly because bigger nets tend to do better so we've built massive nets that run pretty well on big GPUs. But that's the state of things, we have impressive results but from huge neural nets that we really cannot run in realtime on a mobile device. It's particularly bad if you want to run them on a small, battery powered device. Far more so if you only have one image to process, here they're seeing single image speedups of over 10x compared to GPUs (but slower than batched processing) and energy efficiencies about 1000+ times better than mobile GPUs (and far more compared to the beasts in your desktop).
  - nl 11y ago
    But that's the state of things, we have impressive results but from huge neural nets that we really cannot run in realtime on a mobile device. This is absolutely incorrect. A mobile device can execute a pretrained model fine. See, for example [1][2][3]. Google's new TensorFlow NN system is explicitly designed to be able to run on mobile devices and comes with a pre-trained image classification NN that workd fine on mobile devices. This doesn't mean that energy saving is unimportant on a mobile device of course. But there are very widely deployed production systems (eg all of Android) that use them now with no special GPU acceleration. [1] http://googleresearch.blogspot.com.au/2015/07/how-google-translate-squeezes-deep.html http://googleresearch.blogspot.com.au/2015/07/how-google-tra... [2] http://googleresearch.blogspot.com.au/2015/09/google-voice-search-faster-and-more.html http://googleresearch.blogspot.com.au/2015/09/google-voice-s... [3] http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41176.pdf http://static.googleusercontent.com/media/research.google.co...
    - versteegen 11y ago
      Well, it depends on the neural network you want to use. At computer vision/image processing conferences I find that it is much more common for people to use preexisting DNNs and plug them into their systems rather than train their own (why would you waste weeks or months on that?). The good pretrained DNNs available today tend to be huge, requiring a large amount of video RAM. I've talked to people writing realtime image processing software (GPU acceleration required) intended for handheld devices which can't yet actually run on the device because they don't have a suitable DNN which will fit in VRAM.
      - dharma1 11y ago
        mxnet has some optimised models for mobile devices - http://mxnet.readthedocs.org/en/latest/tutorial/smart_device.html http://mxnet.readthedocs.org/en/latest/tutorial/smart_device...
      - nl 11y ago
        3.5% top-5 error rate on ImageNet running on Android. Hard to beat that (unless you are Microsoft Research Asia). https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android https://github.com/tensorflow/tensorflow/tree/master/tensorf...
    - IanCal 11y ago
      > A mobile device can execute a pretrained model fine. Of course it can. The problem is with the size of the network you might want to run. From your first link, which is about single character level image processing > We needed to develop a very small neural net, and put severe limits on how much we tried to teach it—in essence, put an upper bound on the density of information it handles. And from the voice training paper: > While our server-based model has 50M parameters (k = 4, nh = 2560, ni = 26 and no = 7969), to reduce the memory and computation requirement for the embedded model, we experimented with a variety of sizes and chose k = 6, nh = 512, ni = 16 and no = 2000, or 2.7M parameters AlexNet is, what, 60M+? VGG is pretty big too. I guess I wasn't too clear. Yes, there are good results from nets that we can run on mobile devices in realtime. We do, however, want to run significantly larger nets.