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annotate doc/v2_planning/optimization.txt @ 1041:38cc6e075d9b

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PV added to learner committee
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Tue, 07 Sep 2010 20:48:09 -0400
parents 89e76e6e074f
children baf1988db557
rev   line source
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Olivier Delalleau <delallea@iro>
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1 Discussion of Optimization-Related Issues
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2 =========================================
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Xavier Glorot <glorotxa@iro.umontreal.ca>
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4 Members: JB, PL, OD, XG
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5
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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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17
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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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20
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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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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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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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