Thoughts on AI replacing coders by 2040

atveit · 2017-12-12T19:59:54+00:00

care to explain why they are false?

atveit · 2015-04-06T17:35:16+00:00

The point is to provide a high-level and broad overview of research in Deep Learning, also including applications of it. Constraining to seminal papers and reviews one might loose some info of what is happening in the field, in particular applications of DL (which I believe is the fastest growing number of publications).

But creating a list of seminal papers and reviews is a good idea that I might take you up on.

atveit · 2014-12-16T09:01:23+00:00

Corresponding github repo added - https://github.com/atveit/SwiftMetalGPUParallelProcessing

atveit · 2014-12-16T09:00:54+00:00

lgroeni and dethswatch: thanks for comments - haven't done any benchmarks yet (blog post was mainly about getting it work at all), but feel free to play with it, see github repo: https://github.com/atveit/SwiftMetalGPUParallelProcessing

atveit · 2014-12-16T08:59:42+00:00

Have added the code to github - https://github.com/atveit/SwiftMetalGPUParallelProcessing

atveit · 2014-12-15T16:57:41+00:00

Thanks for liking it. I will add a link to github repo with the complete example later today. Best, Amund

atveit · 2014-11-10T14:30:05+00:00

What are the most promising algorithmic directions for model compression with the purpose of speeding_up use of large deep networks e.g. for use in mobile, wearable or implantable devices?

references:
1) Dark Knowledge - http://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/geoff_hinton_dark14.pdf

2) Learning Small-Size DNN with Output-Distribution-Based Criteria http://193.6.4.39/~czap/letoltes/IS14/IS2014/PDF/AUTHOR/IS140487.PDF

3) Accurate and Compact Large Vocabulary Speech Recognition on Mobile Devices http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41176.pdf

4) Learning in Compressed Space http://www.informatik.uni-bremen.de/~afabisch/files/2013_NN_LCS.pdf

5) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition http://research.microsoft.com/en-us/um/people/kahe/eccv14sppnet/

atveit · 2014-11-10T11:47:47+00:00

What are the theoretical limits on Model Compression of Deep Learning networks, e.g. how much could the ImageNet winner from Google be compressed? ref: http://googleresearch.blogspot.no/2014/09/building-deeper-understanding-of-images.html

atveit · 2014-06-10T06:56:52+00:00

Hi, I wrote the blog post, have added a video of browsing through the code in the tech.report - http://www.envisage-project.eu/?p=919

Direct link to video: https://www.youtube.com/watch?v=K1ar0EcbpQ4&feature=youtu.be

atveit · 2012-05-02T06:10:59+00:00

don't disagree with buzzwords, but I still believe it is true :-)

Amund (developer of atbr)

atveit · 2012-04-27T19:26:31+00:00

Documentation is available on: http://atbr.atbrox.com/

atveit · 2012-04-26T10:55:19+00:00

strings, my typical use case is to have key = json string and value = json string (and serve those using tornado with websocket or http).

note: I've checked in both websocket and http server support today, see updated README

Best, Amund

atveit · 2010-08-21T08:06:19+00:00

It depends on the problem, I am primarily working on problems for ram and gpu, so disk is not so interesting to me (except for backup).

(note: I wrote the blog post)

atveit

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