Venue: ICLR 2017 conference

Reviews:

final_score: rating: 2: Strong rejection
review_content: The paper is still extremely poorly written and presented despite multiple reviewers asking to address that issue. The frequent spelling mistakes and incoherent sentences and unclear presentation make reading and understanding the paper very difficult and time consuming. Consider getting help from someone with good english and presentation skills.
review_title: Presentation hinders work.

final_score: rating: 3: Clear rejection
review_content: The authors seems to have proposed a genetic algorithm for learning the features of a convolutional network (LeNet-5 to be precise). The algorithm is validated on some version of the MNIST dataset. 

Unfortunately the paper is extremely hard to understand and it is not at all clear what the exact training algorithm is. Neither do the authors ever motivate why do such a training as opposed to the standard back-prop. What are its advantages/dis-advantages? Furthermore the experimental section is equally unclear. The authors seem to have merged the training and validation set of the MNIST dataset and use only a subset of it. It is not clear why is that the case and what subset they use. In addition, to the best of my understanding, the results reported are RMSE as opposed to classification error. Why is that the case? 

In short, the paper is extremely hard to follow and it is not at all clear what the training algorithm is and how is it better than standard way of training. The experimental section is equally confusing and unconvincing. 

Other comments: 
-- The figures still say LeCun-5
-- The legends of the plots are not in english. Hence I'm not sure what is going on there. 
-- The paper is riddled with typos and hard to understand phrasing. 
review_title: hard to understand what is going on

final_score: rating: 3: Clear rejection
review_content: Unfortunately, this paper is very difficult to understand.  The current version of this paper seems improved compared to the initial version, but still far from a finished level.  I'd encourage the authors to keep editing over the language and presentation.

I also think it would be good to also try answering some of the following questions very clearly in the paper:

- What is the advantage, if any, of the proposed algorithm over SGD?  What is the motivation and goal of the work beyond MNIST benchmarking?

- Why are few training examples used?  Is this a scenario in which the system might have an advantage?

- Concretely describe the genetic algorithms terminology used in the algorithm descriptions, and what each term means in the context of the convolutional network.

- Try to make sure that the method, as described, can be understood by a reader without much prior background on genetic algorithms.

- A single experiment on MNIST is too small to adequately describe the algorithm performance.  Consider using a second or third dataset and/or experimental application.

Much work is still needed on the paper's writing before it can be understood well enough.  I hope that some of this might be useful in helping to improve. I would encourage the authors to try to find outside readers, preferably fluent in English, to work with on a frequent basis before resubmitting to another venue.

review_title: still difficult to understand

final_score: rating: null
review_content: - typo in the title "RECOGNATION".
- Missing words and odd phrasing make reading the paper difficult, please consider having a native speaker review it. And this is after other reviewers have already mentioned this issue and the authors have updated the paper.
- paper is still too long.
review_title: issues

final_score: rating: null
review_content: I do not understand the algorithm and the motivation behind the algorithm proposed? Why is back propagation a bad idea? Is it the computational speed? or convergence issues? If so then why not compare your algorithm with the standard backprop algorithm in the experiments section? 

More generally the paper is extremely poorly written. There are many instance where I do not understand what the authors are trying to say? LeCun-5 is LeNet-5 I believe? 
review_title: Paper Clarity

final_score: rating: null
review_content: This paper is currently fairly long and verbose (at 15 pages, almost twice the recommended length for conference papers, as far as I am aware).  It would be great to edit this down and make it more concise.

Further proof-reading and editing for English would also be a major improvement.  There are many odd phrases and misplaced parts of speech, which unfortunately makes the paper very hard for me to follow.  (For example, "The purpose has ferreted out a novelty approach to train the weights ...", first sentence of Sec 2; ).

For this audience, I suspect much of the background on LeCun-5 and TICA are not so much needed, and those might be a couple places to start making cuts.  The additional background on NSGA-II is nice, as I haven't seen this algorithm before myself, but I think the overall idea to this approach could be more clearly stated early on.  This section also uses terminology that I am not entirely familiar with, and explaining these would help, too.  For example, the initial explaining sentence in sec 1.3 says, NSGA-II "stores all of the information of dominated particles
and the number of domination for each particle".  But what is a "particle" in this application, and what does it mean to be "dominated"?

On a different note, I haven't yet found any discussion comparing this approach to gradient descent or stochastic gradient descent.  I feel a discussion of key differences and advantages would be helpful, as would a direct experimental comparison, since SGD is a simple and very common approach for training a LeCun-5 net.

review_title: Editing

Meta-review: This paper is unfortunately quite unclear and unreadable and nowhere near ready for any conference.
 I would advise the authors to 1) restructure their paper to present first some type of context and identify a problem that they are trying to solve, 2) explain what novel method they propose to solve the identified problem and why this method is promising and how it relates to existing methods, 3) explain what their experiments are trying to do and what the results of the experiments are, 4) enlist someone fluent in English to help with writing and re-reading.
 A way to do this is to find a set of well-cited papers in the same domain with similar ideas and see how they are structured, then try to follow similar outlines.
Decision: Reject