deepnet

57 ms·

1. 661.▲
   by deepnet 11y ago

   This is great work that seems immediately useful to areas like conservation and art. The images were heavily homoginised which was good for continuity but perhaps obscures details. Using the timelapses as a scrubbable index to the original
2. 662.▲
   by deepnet 11y ago

   IMHO too much hubris about how we reason logically and formally led this top-down approach. " Our opinion, and that of the knowledge representation community, is that it is better to provide computer programs with common sense concepts
3. 663.▲
   by deepnet 11y ago

   Using a SIFT pipeline for photogrammettry, I have successfully recreated small objects, Comet 67P and some buildings from quadcopter pics. First I search for features with DoG and match them with SIFT , do a bit extra crawling along matched
4. 664.▲
   by deepnet 11y ago

   Have you guys not banned Neonicotinoides yet ? Since the UK ban my anecdotal evidence is loads more types of bumble bees in my garden. And I have not seen any 'confused' dying bees at all which was a regular sight before the ban.
5. 665.▲
   by deepnet 11y ago

   Pretty comprehensive details here. http://blog.otoro.net/2015/03/28/neural-slime-volleyball/
6. 666.▲
   by deepnet 11y ago

   McKays Information Theory is a brilliant read so far, many thanks. Having learning couched in Information Theory terms brings it all right back to Claude Shannon's early work on Reinforcement Learning Chess programs and Alan Turing&#x2
7. 667.▲
   by deepnet 11y ago

   Many Thanks for the untrained version - I am working on visualising the evolving genome based on your RGBA illustration. I read Sutton and Barto on Q-Learning but only 'got' it when I saw it hand-done with matrix math. http:&#x2F
8. 668.▲
   by deepnet 11y ago

   http://www.iclr.cc/lib/exe/fetch.php?media=iclr2015:silver-i... This slide from David Silver's ICLR talk hint's at Google Deepmind's Gorila Parallel Large Scale Actor Critic Deep Q Architecture Ther
9. 669.▲
   by deepnet 11y ago

   I stand corrected, Schmidhuber in AMA http://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am... has an evolved Atari agent before Mnih
10. 670.▲
    by deepnet 11y ago

    *references Teaching Deep Convolutional Neural Networks to Play Go by Clarke & Storkey http://arxiv.org/abs/1412.3409 Move Evaluation in Go Using Deep Convolutional Neural Networks by Maddison , Huang , Silver &amp
11. 671.▲
    by deepnet 11y ago

    While Deep-Q-Learning is powerful, it uses backprop - your use of CNE genetic evolution rather than backprop may provide a more global search than Mnih's Deep-Q. The methods seem complementary , I have an inkling that the combination m
12. 672.▲
    by deepnet 11y ago

    Mindblowing that the trained agents are 10s of years old - this puts their skill in perspective. Very interesting that we humans may be too 'realtime' to train evolving agents. Do your plank avoiding creatures evolve faster ? htt
13. 673.▲
    by deepnet 11y ago

    Very sorry I have messed things up :( Unfortunately I have not got an edit or delete link so I am powerless to alter my mistake. If any mods* can help delete this rogue copypasta I would be appreciative. [relevant xkcd]( https://x
14. 674.▲
    by deepnet 11y ago

    use the cursor keys for player 2
15. 675.▲
    by deepnet 11y ago

    The number of weights depends on the network architecture. In this case fully connected , 1 weight per connection . 16 inputs to 7 neurons + 7 neurons to 3 outputs + 7 biases 16x7 + 7x3 + 7 = 140 http://blog.otoro.net/wp-con
16. 676.▲
    by deepnet 11y ago

    AFAIK the game is not learning when playing a human. There is a flag to control learning in the javascript trainingMode = false;
17. 677.▲
    by deepnet 11y ago

    [reply to darkmighty comment below, thread depth limitation] Indeed this is exactly it, evolution is a global method, learning is local. I am reading John Gomez's thesis where he compares and combines learning and evolution http:&#x2F
18. 678.▲
    by deepnet 11y ago

    I found the code clear and comprehensible - your genetic library for convnet.js looks useful - have you seen Karpathy's reinforce.js ? I notice you left a training flag - I would like to watch the nets train so definitely going to have
19. 679.▲
    by deepnet 11y ago

    Other networks learn - this network evolved. By breeding the winners of many self-play tournaments a perfect player was born. Training and learning show examples and mark the student against a correct answer - the student network tries to m
20. 680.▲
    by deepnet 11y ago

    Grave's Handwriting Net was trained using Backpropagation - whereby the error between the net's estimate and a training target is sent backward through the net - so the net's estimates gradually become closer to the targets.
21. 681.▲
    by deepnet 11y ago

    Otoro states he is using CNE Conventional Neural Evolution, but combining it with ideas about recurrence from the Atari paper. http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/ He outlines the
22. 682.▲
    by deepnet 11y ago

    The author drops into this reddit thread to answer questions http://www.reddit.com/r/MachineLearning/comments/31eepl/recu...
23. 683.▲
    by deepnet 11y ago

    None of the ideas are new. http://people.idsia.ch/~juergen/evolino.html Yet Schmidhuber's nets are much more complex and certainly different. I still think Otoro's very simple RNN feedback nets are unique - e
24. 684.▲
    by deepnet 11y ago

    Line 60 of slimevolley_pro.js contains the "learned" behaviour in 140 floats: "gene":{"0":7.5719,"1":4.4285,"2":2.2716,"3":-0.3598,"4":-7.8189,"5":-2.5422,&
25. 685.▲
    by deepnet 11y ago

    [accompanying blog post]( http://blog.otoro.net/2015/03/28/neural-slime-volleyball/ ) I too am perplexed as to what the neurons represent, the blog describes the RNN as 19 inputs connected to 7 outputs. Ex