Review paper on machine learning

Review paper on machine learning

This paper provides a review on basics of machine learning, algorithms of machine learning and its applications in various fields 11/19/ · Graduate students Zeren Jiao, Pingfan Hu and Hongfei Xu from the Wang Group are co-authors of the paper. In the article, “Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications,” which originally appeared in the journal ACS Chemical Health & Safety, Wang and his team examined the Learning, Decision Tree, Support Vector Machine (SVM). Cite this Article: Gyana Ranjan Patra, Sarada Prasanna Pati, Mihir Narayan Mohanty, A Review Paper on Machine Learning Algorithms, International Journal of Advanced Research in Engineering and Technology, 11(12), , pp.

Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper

Sign in. Dec 7, · 11 min read. Peer-reviewing is the cornerstone of modern science, and almost all major conferences in machine lea r ning MLsuch as NeurIPS and ICML, rely on it to decide on whether submitted papers are relevant to the community and original enough to be published there. Unfortunately, with the exponentially increasing number of submitted articles over the last ten yearsthe reviewing quality has been dropping just as fast, with one-line reviews became widespread.

Geoffrey Hinton, the famous Turing award winner for his contributions to the machine learning and AI fields, gave one of the reasons review paper on machine learning why this happens in his interview to Wired journal in Anything that makes the brain hurt is not going to get accepted.

While senior reviewers have little to no excuse for such behavior why would you voluntarily agree to review if you do not have time to do it properly?! Conference organizers usually provide helpful guidelines with examples from reviews gathered over the years, but this cannot explain how to write a full review from scratch: starting from reading the submitted article for the first time and to finalizing your review and submitting it on the conference website.

One of its lectures goes as follows: we read the paper together paragraph by paragraph and I explain to what particular parts of it a reviewer has to pay attention. I chose this paper for two reasons: 1 it is not in one of my areas of primary expertise, and 2 it remains largely accessible to anybody having a general background in ML.

I thought that the first point was very important as most of the future Ph. I now propose you to follow me through the paper in order to understand how to write a review for it. To do this, I suggest you read full sections of the paper indicated in the titles below before reading my comments on it. When reading this part, I note every promise made by authors and expect that authors supported it by facts in the main body of their work.

I put important things in bold here, review paper on machine learning. What information this abstract gives me as a reviewer? First, it defines the general area of the submission, which is the study of attention mechanisms in DCNs. I note it and move on to the introduction. The introduction is an extended version of the abstract that includes hints to previous works and provides more details on the proposed contribution. In this paper, the introduction contains several things that attract my attention.

First, I identify several closely related prior works mentioned numerous times in the second paragraph, namely: Linsley et al. As a reviewer, I would now briefly go through the contents of these two papers with a particular emphasis on the first one because 1 it is more recent and most likely will include a comparison to other related works mentioned in the introduction, and 2 the authors compare to it singularly.

Second, review paper on machine learning, I review paper on machine learning the positioning of proposed contributions w. state-of-the-artnamely: 1 the authors propose a more efficient strategy implemented on ClickMe. ai platform to obtain attention maps for large-scale datasets when compared to Salicon dataset and Linsley et al. Once again, review paper on machine learning, as a reviewer, I will now seek arguments that support each of these claims.

This section is very important, as it is entirely devoted to supporting the first claim mentioned above. On review paper on machine learning one hand, it is supposed to show that the proposed strategy used to collect attention maps scales better than previous work. Here is my resume for the first part. You may note that I put the ClickMe. ai strategy proposed by the authors as a strength of the paper as it involves only one human being contrary to two from Linsley et al.

A downside to this is that the comparison with Linsley at al. allows me to discover the identities of the authors who mention ClickMe. ai in their previous paper, review paper on machine learning.

Here is my summary of the second claim. the maps from the Salicon dataset, review paper on machine learning. So far, I am only praising the strengths of the paper, but is there something to say about the weaknesses?

Here are some of my remarks to be included in the review. The authors say that the ClickMe game scales better than the Clicktionary game of Linsley et al. Is it reasonable? Why do we consider these maps useful later? I note it and move to Section 3. I am not an expert in attention mechanisms for DCNs, and I cannot judge the soundness of what is proposed by the authors and its novelty.

Despite this, review paper on machine learning, I still notice the following phrase of the authors:. They do not explain how they choose these layers, while omitting low-level layers altogether: an ablative study might be useful to back it up in this context. One more thing to add to the review as such discussion can be highly useful for researchers who may decide to implement their module for architectures other than ResNet This section presents most of the experimental results for the architecture proposed in Section 3 with additional regularization that forces learned maps to look similar to those provided by human participants of ClickMe.

Here is my short resume for this part. I note that most of the results indeed seem to back up the third claim from authors: human-in-the-loop supervision improves the performance on popular object recognition datasets. Even though I would tend to be convinced by the experimental results, I still notice several inconsistencies, review paper on machine learning. The first remark is rather obvious. Why using the magic number 6 for the regularization parameter? The second question is related to Table 1 from the paper that shows significant improvement in terms of both the classification accuracy on the ILSVRC12 dataset and the ability to learn features similar to ClickMe maps.

Well, the latter improvement seems to be quite obvious to me as it merely indicates that the regularization forcing learned features to look like ClickMe maps works well, review paper on machine learning. Other baselines do not particularly seek to force such behavior, and this performance gain should be presented rather as an argument justifying the chosen regularization strength. Thirdthe authors mention that with a reduced set of ClickMe maps Table 4 in Appendixreview paper on machine learning method also performs better than all other baselines, but one can see that in this case, the performance gap becomes very small.

After explaining how I went through this paper, it is now time to put it all into a review ready to be submitted to the conference website. As required by many conferences, I start with the summary of the paper. Note that the summary is very important as it shows the authors that you understand their work.

Then, I provide its strengths and weakness. I find it crucial to give some positive feedback even if I plan to suggest rejecting the paper in the end.

This shows the authors what parts of their work were appreciated by the reviewers. I then continue with several detailed comments. In the case of this submission, you can check the reviews here. This is very important as an unknowledgeable reviewer with high confidence is a nightmare for both the authors and the ACs. And it goes the other way around too: if you review a paper from your narrow area of expertise, you should clearly indicate it so review paper on machine learning the AC will be able to identify the most informative reviews.

I did the review of the first submitted version of this paper on purpose so that you can see the camera-ready version submitted by the authors once their paper was accepted. This last phrase is what I see as a major source of bad reviews. The first approach to reviews takes time, requires patience and more than a pinch of goodwill.

The second requires none of it and leads to a destructive half-random reviewing process where it can take years for important contributions to be actually published. Luckily, however, it is up to all of us to choose how we want it to be in the end. This article explains my approach to reviewing papers, but I am not the highest authority in this matter and I do not claim that it is the only right way to do it.

There can be other opinions on how a good review should look like, as well as people who will find my reviews bad and uninformative. Also, there are different types of papers and reviewing a theoretical research paper may be very different from reviewing an applied research paper. The goal of this article was to show one possible way of how to do review paper on machine learning hoping that it can be helpful for those who will find it suitable personally to them.

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A Medium publication sharing concepts, ideas and codes. Get started. Open in app. Sign in Get started. Editors' Picks Features Deep Dives Grow Contribute. Get started Open in app. Reviewing for Machine Learning Conferences Explained, review paper on machine learning. From reading a paper for the first time to writing its complete review in a single Medium article. Ievgen Redko. Sign up for The Variable.

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Machine Learning Application In Healthcare : A Research paper review

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[PDF] Machine Learning Algorithms : A Review | Semantic Scholar

11/19/ · Graduate students Zeren Jiao, Pingfan Hu and Hongfei Xu from the Wang Group are co-authors of the paper. In the article, “Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications,” which originally appeared in the journal ACS Chemical Health & Safety, Wang and his team examined the of Machine Learning) have won several contests in pattern recognition and machine learning. This This review comprehe nsively summarises relevant studies, much of it from prior state-of-the-art Objective: This paper has been prepared as an effort to reassess the research studies on the relevance of machine learning techniques in the domain of agricultural crop production. Methods/Statistical Analysis: This method is a new approach for production of agricultural crop management. Accurate and timely forecasts of crop production are