Deep Learning Research Directions: Computational Efficiency by Tim Dettmers 15 Comments This blog post looks at the growth of computation, data, deep learning researcher demographics to show that the field of deep learning could stagnate over slowing growth. We will look at recent deep learning research papers which strike up similar problems but also demonstrate how one could to solve these problems.
The procedure As a part of your paper publication, you can start documenting the 'existing techniques' from the scrap journal you did during the studies.
Here you have to extract what all are the techniques existing as a solution for the particular problem and the pros and cons of those. Next, document the 'introduction' about what is the topic and what you are going to do.
Better to keep it short. Follows your contribution and the simulated results. Describe the problem 2. State your contributions 'Abstract' is one section you can work on in the last, as it has to cover the all the sections very briefly.
Please note that Abstract makes the committee members to decide whether or not to read your paper. Generally four lines are sufficient for this. State the problem 2. Say why it's an interesting problem 3.
Say what your solution achieves 4. Say what follows from your solution Section by section The divide-and-conquer strategy works on a day-to-day level as well. Instead of writing an entire paper, focus on the goal of writing a section, or outline.
Remember, every task you complete gets you closer to finishing your paper. Get a pre-review Now your paper is ready.
You can ask your peers or professors to review your paper. Next is to find the right place to publish it. You can start of with national level conferences, which often gets conducted in many universities. Then once you gain a level of confidence, you can proceed to international conferences and journals.
Read the reviews carefully This is really, really, really hard. Only a small proportion, 5 to 10 percent, are accepted the first time they are submitted, and usually they are only accepted subject to revision.
In fact, anything aside from simply "reject," Neal-Barnett reminds, is a positive review. Though not as good as revise and resubmit, "they still want the paper!
Don't panic After reading the review the first time, put it aside. Come back to it later, reading the paper closely to decide whether the criticisms were valid and how you can address them.
You will often find that reviewers make criticisms that are off-target because they misinterpreted some aspect of your paper.
If so, don't let it get to you -- just rewrite that part of your paper more clearly so that the same misunderstanding won't happen again. It's frustrating to have a paper rejected because of a misunderstanding, but at least it's something you can fix.
On the other hand, criticisms of the content of the paper may require more substantial revisions -- rethinking your ideas, running more tests, or redoing an analysis.
Be Positive If your paper is rejected, keep trying! Take the reviews to heart and try to rewrite the paper, addressing the reviewer's comments. Personality Processes and Individual Differences. Wrong sequence in Figure and Table numbering Misalignment of columns Usage of figures from another paper without credit and permission Where to publish Generally, there are three main choices: A conference is the right place for beginner scholars, since the level of scrutiny is minimal.
The conferences will accept papers which details about the comparison of existing technologies, mathematically proven but practically unproven proposals, etc. A conference is the good play ground for Intermediated scholars.
This mostly same as National Conference but the securitization will be more.Tips for a Successful Recess Susan Meyer offers tips to help ensure a safe and productive recess period in the middle grades.
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This blog post looks at the growth of computation, data, deep learning researcher demographics to show that the field of deep learning could stagnate over slowing growth. We will look at recent deep learning research papers which strike up similar problems but also demonstrate how one could to solve.