Mercurial > pylearn
annotate doc/v2_planning/optimization.txt @ 1030:a154c9b68239
dataset: Dumi confirmed as leader
author | Olivier Delalleau <delallea@iro> |
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date | Mon, 06 Sep 2010 20:45:45 -0400 |
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1 Discussion of Optimization-Related Issues |
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2 ========================================= |
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4 Members: JB, PL, OD |
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6 Representative: JB |
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9 Previous work - scikits, openopt, scipy provide function optimization |
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10 algorithms. These are not currently GPU-enabled but may be in the future. |
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13 IS PREVIOUS WORK SUFFICIENT? |
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14 -------------------------------- |
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16 In many cases it is (I used it for sparse coding, and it was ok). |
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18 These packages provide batch optimization, whereas we typically need online |
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19 optimization. |
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21 It can be faster (to run) and more convenient (to implement) to have |
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22 optimization algorithms as Theano update expressions. |
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23 |
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25 What optimization algorithms do we want/need? |
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26 --------------------------------------------- |
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28 - sgd |
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29 - sgd + momentum |
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30 - sgd with annealing schedule |
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31 - TONGA |
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32 - James Marten's Hessian-free |
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33 - Conjugate gradients, batch and (large) mini-batch [that is also what Marten's thing does] |
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34 |
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35 Do we need anything to make batch algos work better with Pylearn things? |
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36 - conjugate methods? yes |
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37 - L-BFGS? maybe, when needed |
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38 |
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41 |