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annotate doc/v2_planning/optimization.txt @ 1095:520fcaa45692

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author Olivier Delalleau <delallea@iro>
date Mon, 13 Sep 2010 09:15:25 -0400
parents 2cf3ad953bf9
children 7c5dc11c850a
rev   line source
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1 =========================
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2 Optimization for Learning
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3 =========================
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4
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5 Members: Bergstra, Lamblin, Delalleau, Glorot, Breuleux, Bordes
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6 Leader: Bergstra
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7
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9
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10 Initial Writeup by James
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11 =========================================
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12
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14
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15 Previous work - scikits, openopt, scipy provide function optimization
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16 algorithms. These are not currently GPU-enabled but may be in the future.
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17
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18
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19 IS PREVIOUS WORK SUFFICIENT?
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20 --------------------------------
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21
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22 In many cases it is (I used it for sparse coding, and it was ok).
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23
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24 These packages provide batch optimization, whereas we typically need online
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25 optimization.
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26
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27 It can be faster (to run) and more convenient (to implement) to have
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28 optimization algorithms as Theano update expressions.
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30
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31 What optimization algorithms do we want/need?
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32 ---------------------------------------------
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33
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34 - sgd
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35 - sgd + momentum
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36 - sgd with annealing schedule
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37 - TONGA
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38 - James Marten's Hessian-free
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39 - Conjugate gradients, batch and (large) mini-batch [that is also what Marten's thing does]
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40
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41 Do we need anything to make batch algos work better with Pylearn things?
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42 - conjugate methods? yes
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43 - L-BFGS? maybe, when needed
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44
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45
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46
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48
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49 Proposal for API
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50 ================
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51
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52 See api_optimization.txt.
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54 OD: Do we really need a different file? If yes, maybe create a subdirectory to
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55 be able to easily find all files related to optimization?