annotate writeup/nips_rebuttal_clean.txt @ 574:d12b9a1432e8

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author Dumitru Erhan <dumitru.erhan@gmail.com>
date Sat, 07 Aug 2010 18:39:36 -0700
parents
children bff9ab360ef4
rev   line source
574
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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1 Reviewer_1 claims that handwriting recognition is essentially solved, and we
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Dumitru Erhan <dumitru.erhan@gmail.com>
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2 believe that this is not true. Indeed, the best methods have been
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Dumitru Erhan <dumitru.erhan@gmail.com>
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3 getting essentially human performance in the case of clean digits. We are not
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Dumitru Erhan <dumitru.erhan@gmail.com>
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4 aware of previous papers showing that human performance has been reached on the
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Dumitru Erhan <dumitru.erhan@gmail.com>
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5 full character set. Furthermore, it is clear from our own experimentation that
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Dumitru Erhan <dumitru.erhan@gmail.com>
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6 humans still greatly outperform machines when the characters are heavily
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Dumitru Erhan <dumitru.erhan@gmail.com>
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7 distorted (e.g. the NISTP dataset). Playing with the provided demo will
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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8 quickly convince you that this is true.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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9
d12b9a1432e8 cleaned-up version, fewer typos, shortened (but need 700 chars less)
Dumitru Erhan <dumitru.erhan@gmail.com>
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10 "...not intended to compete with the state-of-the-art...": We actually included
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Dumitru Erhan <dumitru.erhan@gmail.com>
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11 comparisons with the state-of-the-art on the NIST dataset (and beat it).
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Dumitru Erhan <dumitru.erhan@gmail.com>
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12
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Dumitru Erhan <dumitru.erhan@gmail.com>
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13 "the demonstrations that self-taught learning can help deep learners is
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Dumitru Erhan <dumitru.erhan@gmail.com>
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14 helpful": indeed, but it is even more interesting to consider the result that
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Dumitru Erhan <dumitru.erhan@gmail.com>
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15 self-taught learning was found *more helpful for deep learners than for shallow
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Dumitru Erhan <dumitru.erhan@gmail.com>
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16 ones*. Since the availability of out-of-distribution data is common (especially
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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17 out-of-class data), this is of practical importance.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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18
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Dumitru Erhan <dumitru.erhan@gmail.com>
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19 Reviewer_4: "It would also be interesting to compare to SVMs...": ordinary SVMs cannot be
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Dumitru Erhan <dumitru.erhan@gmail.com>
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20 used on such large datasets, and indeed it is a good idea to explore variants of
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Dumitru Erhan <dumitru.erhan@gmail.com>
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21 SVMs or approximations of SVMs. We will continue exploring this thread (and the
d12b9a1432e8 cleaned-up version, fewer typos, shortened (but need 700 chars less)
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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22 particular suggestion made) and hope to include these results in the final
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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23 paper, to add more shallow learners to the comparison.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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24
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Dumitru Erhan <dumitru.erhan@gmail.com>
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25 "...it would be helpful to provide some theoretical analysis...": indeed, but
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Dumitru Erhan <dumitru.erhan@gmail.com>
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26 this is either mathematically challenging (to say the least, since deep models
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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27 involve a non-convex optimization) or would likely require very strong
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Dumitru Erhan <dumitru.erhan@gmail.com>
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28 assumptions on the data distribution. However, there exists
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Dumitru Erhan <dumitru.erhan@gmail.com>
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29 theoretical literature which answers some basic questions about this issue,
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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30 starting with the work of Jonathan Baxter (COLT 1995) "Learning internal
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Dumitru Erhan <dumitru.erhan@gmail.com>
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31 representations". The argument is about capacity
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Dumitru Erhan <dumitru.erhan@gmail.com>
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32 and sharing it across tasks so as to achieve better generalization. The lower
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Dumitru Erhan <dumitru.erhan@gmail.com>
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33 layers implement features that can potentially be shared across tasks. As long
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Dumitru Erhan <dumitru.erhan@gmail.com>
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34 as some sharing is possible (because the same features can be useful for several
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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35 tasks), then there is a potential benefit from shared
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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36 internal representations. Whereas a one-hidden-layer MLP can only share linear
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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37 features, a deep architecture can share non-linear ones which have the potential
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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38 for representing more abstract concepts.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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39
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Dumitru Erhan <dumitru.erhan@gmail.com>
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40 Reviewer_5 about semi-supervised learning: In the unsupervised phase, no labels
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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41 are used. In the supervised fine-tuning phase, all labels are used, so this is
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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42 not the semi-supervised setting. This paper did not examine the potential
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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43 advantage of exploiting large quantities of additional unlabeled data, but the
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Dumitru Erhan <dumitru.erhan@gmail.com>
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44 availability of the generated dataset and of the learning setup would make it
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Dumitru Erhan <dumitru.erhan@gmail.com>
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45 possible to easily conduct a study to answer this interesting
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Dumitru Erhan <dumitru.erhan@gmail.com>
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46 question. Note however that previous work [5] already investigated the relative
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Dumitru Erhan <dumitru.erhan@gmail.com>
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47 advantage of the semi-supervised setting for deep vs shallow architectures,
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Dumitru Erhan <dumitru.erhan@gmail.com>
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48 which is why we did not focus on this here. It might still be worth to do these
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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49 experiments because the deep learning algorithms were different.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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50
d12b9a1432e8 cleaned-up version, fewer typos, shortened (but need 700 chars less)
Dumitru Erhan <dumitru.erhan@gmail.com>
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51 "...human errors may be present...": Indeed, there are variations across human
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Dumitru Erhan <dumitru.erhan@gmail.com>
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52 labelings, which have have estimated (since each character
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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53 was viewed by 3 different humans), and reported in the paper (the standard
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Dumitru Erhan <dumitru.erhan@gmail.com>
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54 deviations across humans are large, but the standard error across a large test
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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55 set is very small, so we believe the average error numbers to be fairly
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Dumitru Erhan <dumitru.erhan@gmail.com>
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56 accurate).
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Dumitru Erhan <dumitru.erhan@gmail.com>
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57
d12b9a1432e8 cleaned-up version, fewer typos, shortened (but need 700 chars less)
Dumitru Erhan <dumitru.erhan@gmail.com>
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58 "...authors do cite a supplement, but I did not have access to it...": that is
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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59 strange. We could (and still can) access it from the CMT web site. We will make
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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60 sure to include a complete pseudo-code of SDAs in it.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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61
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Dumitru Erhan <dumitru.erhan@gmail.com>
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62 "...main contributions of the manuscript...": the main
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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63 contribution is actually to show that the self-taught learning setting is more
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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64 beneficial to deeper architectures.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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65
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Dumitru Erhan <dumitru.erhan@gmail.com>
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66 "...restriction to MLPs...": that restriction was motivated by the computational
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Dumitru Erhan <dumitru.erhan@gmail.com>
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67 challenge of training on hundreds of millions of examples. Apart from linear
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Dumitru Erhan <dumitru.erhan@gmail.com>
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68 models (which do not fare well on this task), it is not clear to us what
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Dumitru Erhan <dumitru.erhan@gmail.com>
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69 could be used, and so MLPs were the
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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70 obvious candidates to compare with. We will explore the use of SVM
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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71 approximations, as suggested by Reviewer_1. Other suggestions are welcome.
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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72
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Dumitru Erhan <dumitru.erhan@gmail.com>
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73 "Reviewer 6:...novelty [..] is somewhat marginal since [...] reminiscent of
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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74 prior work on character recognition using deformations and transformations".
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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75 The main originality is in showing that deep learners can take more advantage
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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76 than shallow learners of such data and of the self-taught learning framework in
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Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
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77 general.
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Dumitru Erhan <dumitru.erhan@gmail.com>
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78