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milakov.org

Maxim Milakov

Maxim Milakov is a researcher in machine learning and high-performance computing

http://www.milakov.org/

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Maxim Milakov

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Yar●●●avl , 150054

RU

7.90●●●●0310
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Maxim Milakov

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Yar●●●avl , 150054

RU

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ma●●●●●●●●●●●@gmail.com

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Maxim Milakov

Lenin●●●●●b, 51

Yar●●●avl , 150054

RU

7.90●●●●0310
ma●●●●●●●●●●●@gmail.com

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Maxim Milakov | milakov.org Reviews
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Maxim Milakov is a researcher in machine learning and high-performance computing
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Maxim Milakov | milakov.org Reviews

https://milakov.org

Maxim Milakov is a researcher in machine learning and high-performance computing

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1

Maxim Milakov: October 2014

http://www.milakov.org/2014_10_01_archive.html

A researcher in machine learning and high-performance computing. Oct 4, 2014. NnForge v1.1.9. More sparse cases supported in GPU backend for convolutional layers, improved perf. Convert data type transformer added. Hessian based learning algo is removed. Galaxy Zoo example removed. Use previous releases to get it. Reporting average weights/updates after each batch. Image classifier demo added, improved perf for running single entry through the tester. Subscribe to: Posts (Atom). NnForge v1.1.9.

2

Maxim Milakov: November 2014

http://www.milakov.org/2014_11_01_archive.html

A researcher in machine learning and high-performance computing. Nov 30, 2014. NnForge v1.1.11. Hi, I am releasing nnForge. V1111 with a number of significant changes:. Padding added to sparse convolutional layers. Sparse convolutional layers implemented in GPU backend (Kepler only). Fixed bug with dropout when error function is fuzed with last activation function. Array with random numbers extended to 256K elements (for dropout). Nov 3, 2014. NnForge v1.1.10. Hi, here is nnForge. NnForge v1.1.11.

3

Maxim Milakov: May 2014

http://www.milakov.org/2014_05_01_archive.html

A researcher in machine learning and high-performance computing. May 18, 2014. NnForge v1.1.5. I ported performance improvemetns for forward propagation of convolutional layers in GPU backend to hessian calculators and weight updaters. Get latest release of nnForge. Performance of weight updaters for convolutional layers is improved in CUDA backend. Mostly for Kepler architecture. The increase is much smaller than for forward prop, I got 800 GFLOPs on training single Galaxy Zoo. Network on GeForce Titan.

4

Maxim Milakov: nnForge v1.2.0

http://www.milakov.org/2015/04/nnforge-v120.html

A researcher in machine learning and high-performance computing. Apr 30, 2015. NnForge v1.2.0. Hi, this is a pretty big release of nnForge. The most important improvement is that mode schemas are now stored in Protobuf. Format. You now define the schema via plain text file. Use convert schema action to convert from old binary format to new one. I also implemented Overfeat. Functionality - this allows running inference on large input data with fine-frained results efficiently. All the change are:.

5

Maxim Milakov: August 2014

http://www.milakov.org/2014_08_01_archive.html

A researcher in machine learning and high-performance computing. Aug 23, 2014. NnForge v1.1.8. Sparse (in feature map dimension) convolutional layer added, with full support in CPU backend and fully connected (spatial) 1x1 support in GPU backend. You can use -std=c 11 now with CUDA 6.5 toolkit. GTSRB switched to batch training. Boost and OpenCV libs default paths are /usr now. Improved performance for 1x1 convolutions in GPU backend. Subscribe to: Posts (Atom). NnForge v1.1.8.

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A researcher in machine learning and high-performance computing. Nov 30, 2016. NnForge v2.3.0. I have added multi-GPU support to nnForge. Both training and inferene can be done on multiple GPUs now. Single node only is supported. Training is parallelized with data parallel approach, where mini-batch is split across multiple GPUs. The framework moved to C 11 now, you will need gcc 4.7 or newer to build the lib, and MS VS 2013 for Windows. Jul 5, 2016. NnForge v2.2.0. W/out bias option added. Feb 21, 2016.

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