Mercurial > ift6266

annotate writeup/aistats_review_response.txt @ 623:d44c78c90669

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
entered revisions for AMT and SVMs
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sun, 09 Jan 2011 22:00:39 -0500
parents 5c67f674d724
children 49933073590c
rev   line source
616
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1
619
ea31fee25147 review response
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 618
diff changeset
2 We thank the authors for their thoughtful comments. Please find our responses below.
616
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
3
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
4 * Comparisons with shallower networks, but using unsupervised pre-training:
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
5 e will add those results to the paper. Previous work in our group with
620
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
6 very similar data (the InfiniteMNIST dataset were published in JMLR in 2010
616
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
7 "Why Does Unsupervised Pre-training Help Deep Learning?"). The results indeed
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
8 show improvement when going from 1 to 2 and then 3 layers, even when using
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
9 unsupervised pre-training (RBM or Denoising Auto-Encoder).
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
10
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
11 * Comparisons with SVMs. We have tried several kinds of SVMs. The main limitation
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
12 of course is the size of the training set. One option is to use a non-linear SVM
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
13 with a reduced training set, and the other is to use an online linear SVM.
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
14 Another option we have considered is to project the input non-linearly in a
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
15 high-dimensional but sparse representation and then use an online linear SVM on that space.
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
16 For this experiment we have thresholded input pixel gray levels considered a
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
17 low-order polynomial expansion (e.g. only looking at pairs of non-zero pixels).
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
18 We have obtained the following results until now, all substantially worse than those
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
19 obtained with the MLP and deep nets.
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
20
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
21 SVM type training set input online validation test set
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
22 type / size features training set error error
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
23 error
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
24 Linear SVM, NIST, 651k, original, 36.62%, 34.41%, 42.26%
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
25 Linear SVM, NIST, 651k, sparse quadratic, 30.96%, 28.00%, 41.28%
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
26 Linear SVM, NISTP, 800k, original, 88.50%, 85.24%, 87.36%
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
27 Linear SVM, NISTP, 800k, sparse quadratic, 81.76%, 83.69%, 85.56%
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
28 RBF SVM, NISTP, 100k, original, 74.73%, 56.57%, 64.22%
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
29
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
30 The best results were obtained with the sparse quadratic input features, and
623
d44c78c90669 entered revisions for AMT and SVMs
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 620
diff changeset
31 training on the CLEAN data (NIST) rather than the perturbed data (NISTP).
d44c78c90669 entered revisions for AMT and SVMs
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 620
diff changeset
32 A summary of the above results was added to the revised paper.
616
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
33
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
34
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
35 * Using distorted characters as the corruption process of the Denoising
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
36 Auto-Encoder (DAE). We had already performed preliminary experiments with this idea
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
37 and it did not work very well (in fact it depends on the kind of distortion
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
38 considered), i.e., it did not improve on the simpler forms of noise we used
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
39 for the AISTATS submission. We have several interpretations for this, which should
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
40 probably go (along with more extensive simulations) into another paper.
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
41 The main interpretation for those results is that the DAE learns good
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
42 features by being given as target (to reconstruct) a pattern of higher
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
43 density (according to the unknown, underlying generating distribution) than
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
44 the network input. This is how it gets to know where the density should
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
45 concentrate. Hence distortions that are *plausible* in the input distribution
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
46 (such as translation, rotation, scaling, etc.) are not very useful, whereas
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
47 corruption due to a form of noise are useful. In fact, the most useful
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
48 is a very simple form of noise, that guarantees that the input is much
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
49 less likely than the target, such as Gaussian noise. Another way to think
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
50 about it is to consider the symmetries involved. A corruption process should
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
51 be such that swapping input for target should be very unlikely: this is
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
52 true for many kinds of noises, but not for geometric transformations
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
53 and deformations.
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
54
618
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
55 * Human labeling: We controlled noise in the labelling process by (1)
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
56 requiring AMT workers with a higher than normal average of accepted
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
57 responses (>95%) on other tasks (2) discarding responses that were not
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
58 complete (10 predictions) (3) discarding responses for which for which the
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
59 time to predict was smaller than 3 seconds for NIST (the mean response time
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
60 was 20 seconds) and 6 seconds seconds for NISTP (average response time of
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
61 45 seconds) (4) discarding responses which were obviously wrong (10
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
62 identical ones, or "12345..."). Overall, after such filtering, we kept
623
d44c78c90669 entered revisions for AMT and SVMs
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 620
diff changeset
63 approximately 95% of the AMT workers' responses. The above paragraph
d44c78c90669 entered revisions for AMT and SVMs
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 620
diff changeset
64 was added to the revision. We thank the reviewer for
618
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
65 the suggestion about multi-stage questionnaires, we will definitely
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
66 consider this as an option next time we perform this experiment. However,
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
67 to be fair, if we were to do so, we should also consider the same
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
68 multi-stage decision process for the machine learning algorithms as well.
14ba0120baff review response changes
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 616
diff changeset
69
620
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
70 * Size of labeled set: in our JMLR 2010 paper on deep learning (cited
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
71 above), we already verified the effect of number of labeled examples on the
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
72 deep learners and shallow learners (with or without unsupervised
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
73 pre-training); see fig. 11 of that paper, which involves data very similar
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
74 to those studied here. Basically (and somewhat surprisingly) the deep
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
75 learners with unsupervised pre-training can take more advantage of a large
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
76 amount of labeled examples, presumably because of the initialization effect
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
77 (that benefits from the prior that representations that are useful for P(X)
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
78 are also useful for P(Y|X)), and the effect does not disappear when the
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
79 number of labeled examples increases. Other work in the semi-supervised
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
80 setting (Lee et al, NIPS2009, "Unsupervised feature learning...") also show
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
81 that the advantage of unsupervised feature learning by a deep architecture
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
82 is most pronounced in the semi-supervised setting with very few labeled
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
83 examples. Adding the training curve in the self-taught settings of this AISTAT
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
84 submission is a good idea, but probably unlikely to provide results
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
85 different from the above already reported in the literature in similar
5c67f674d724 more changes to rebuttal
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents: 619
diff changeset
86 settings.
616
b0cdd200b2bd added aistats_review_response.txt
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
87