Add higher-order derivative support of Chainer to the comparison table #3477
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delta2323
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delta2323
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How about writing a note describing why this is "partial"? |
niboshi
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I added comments. |
niboshi
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LGTM. |
mitmul
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Mar 8, 2018
docs/source/comparison.rst
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| @@ -37,8 +37,9 @@ This table compares Chainer with other actively developed deep learning framewor | |||
| .. [1] Define-by-run is in development as of June 2017 and tracked in `dmlc/mxnet#5705 <https://github.com/dmlc/mxnet/pull/5705>`_. It is also possible using the much slower MinPy extension. | |||
| .. [2] Symbolic autograd is in development as of June 2017 and tracked in `deeplearning4j/nd4j#1750 <https://github.com/deeplearning4j/nd4j/pull/1750>`_. | |||
| .. [3] Symbolic autograd is available only with ngraph backend (experimental). | |||
| .. [4] Nervana provides kernels that are meant to compete with cuDNN. | |||
| .. [5] Multiprocessing provides a significant performance improvement only for frameworks that use Python at runtime. | |||
| .. [4] Some functions do not support higher-order differentiation. See `chainer/chainer#2970 <https://github.com/chainer/chainer/pull/2970>`_. | |||
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Could you replace this with #4449 ?
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Thank you for your review. I applied your suggestion. |
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Jenkins, test this please |
mitmul
approved these changes
Mar 30, 2018
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LGTM |
kmaehashi
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Apr 17, 2018
Add higher-order derivative support of Chainer to the comparison table
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This PR updates the comparison table to add partial support of higher-order derivative to the Chainer slot.