--- a/writeup/nips_rebuttal.txt	Sat Aug 07 18:39:36 2010 -0700
+++ b/writeup/nips_rebuttal.txt	Sat Aug 07 22:46:12 2010 -0400
@@ -9,16 +9,16 @@
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 |||The paper is well-written and the contributions are presented clearly. However, this paper only presents the results established methods to an application that is already essentially solved. 
 
-Reviewer_1 claims that handwriting recognition is essentially solved, and we believe that this is not true. It is true that the best methods have been getting essentially human performance in the case of clean digits. We are not aware of previous papers showing that human performance has been reached on the full character set. Furthermore, it is clear from our own experimentation that humans still greatly outperform machines when the characters are heavily distorted (e.g. as in our NISTP dataset). Playing with the provided demo will quickly convince you that this is true.
+Reviewer_1 claims that handwriting recognition is essentially solved: we believe this is not true. Yes the best methods have been getting essentially human performance in the case of clean digits. But we are not aware of previous papers achieving human performance on the full character set. It is clear from our own experimentation (play with the demo to convince yourself) that humans still clearly outperform machines when the characters are heavily distorted (e.g. as in our NISTP dataset). 
 
 |||While the experiments were run thoroughly and engineered well, the results are not intended to compete with the state-of-the-art, so this is not an application paper. While the main conclusion -- that self-taught learning helps deep learners -- is somewhat interesting, it is not shown to apply generally, and even so is not significant enough to merit acceptance since both the models and self-taught learning methods have been previously shown to be useful (albeit separately). 
 
-"...not intended to compete with the state-of-the-art...": We actually included comparisons with the state-of-the-art on the NIST dataset (and beat it).
+"...not intended to compete with the state-of-the-art...": We had included comparisons with the state-of-the-art on the NIST dataset (and beat it).
 
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 |||Because the experiments were run well, the new datasets are useful contributions, and the demonstration that self-taught learning can help deep learners is helpful, it would be good for other researchers to see this work. It would be appropriate for a workshop or technical report, or as part of a review or survey paper.
 
-"the demonstrations that self-taught learning can help deep learners is helpful": indeed, but it is even more interesting to consider the result that self-taught learning was found MORE HELPFUL FOR DEEP LEARNERS THAN SHALLOW ONES. Since the availability of out-of-distribution data is common (especially out-of-class data), this is of practical importance.
+"the demonstrations that self-taught learning can help deep learners is helpful": indeed, but it is even more interesting to consider the result that self-taught learning was found MORE HELPFUL FOR DEEP LEARNERS THAN SHALLOW ONES. Since out-of-distribution data is common (especially out-of-class data), this is practically important.
 
 |||Please summarize your review in 1-2 sentences	 Since there is no technical or methodological contribution, this paper should not be accepted to this conference.
 |||Masked Reviewer ID:	Assigned_Reviewer_4
@@ -33,7 +33,7 @@
 |||Another interesting observation is that deep learners benefit more from multi-task learning compared to shallow multi-layer perceptrons. It would also be interesting to compare to SVMs that are built incrementally, i.e. fit SVMs using a subset of data, retain support vectors, add more data, etc. This would better justify empirical findings. 
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-"It would also be interesting to compare to SVMs...": ordinary SVMs cannot be used on such large datasets, and indeed it is a good idea to explore variants of SVMs or approximations of SVMs. We will continue exploring this thread (and the particular suggestion made) and hope to include these results in the final paper, to add more shallow learners to the comparison.
+Reviewer_4, "It would also be interesting to compare to SVMs...": ordinary SVMs cannot be used on such large datasets. We will explore SVM variants such as the suggestion made to add SVM results to the paper.
 
 |||While the paper is mostly empirical, it would be helpful to provide some theoretical analysis. It would be interesting to work out under what conditions one would expect deep models to benefit from out-of-distribution examples (obviously if the distribution of those examples is very different, it would naturally hurt model performance), or when one would expect deep models to benefit more from multi-task setting compared to shallow learners. 
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