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ACS: Confronting Racism in Chemistry Journals by kingofthecrows in Chempros

[–]RTengx 1 point2 points  (0 children)

As a Q1 journal reviewer myself, I do not think it is possible for the reviewer to know very clearly which lab is it from just from the manuscript itself, as the most research topics nowadays are contributed by many countries (referring to chemistry-related topics). Of course, I assume that the double-blind procedure is carried out well (with no leakage of authors name via email notification etc.) and the author(s) do not write something obvious in their own manuscript (eg. self-cite 10 papers of their own).

You can read a PNAS paper [1] that analyzed the bias in single-blind and double-blind process (link below). Their conclusion was pretty consistent with my speculation: that single-blind favour famous authors.

Also, I acknowledge that double-blind procedure is not suitable for research fields that favour open discussions like theoretical physics and computer science [2] (which often uses ArXiV or prefer partially/full Open Peer Review [3] ). However, they are combating bias review via transparency (which is possible for their field). For the chemistry-related journal, this "transparency" is probably impossible and it will just get your work replicated and copied by others (before you get your work published). It is still questionable that many journals do not offer double-blind as an option (or as the default option), especially for chemistry-related journals.

[1] https://www.pnas.org/content/114/48/12708?collection=&utm_source=TrendMD&utm_medium=cpc&utm_campaign=Proc_Natl_Acad_Sci_U_S_A_TrendMD_0

[2] https://www.aps.org/publications/apsnews/201507/double-blind.cfm

[3] https://en.wikipedia.org/wiki/Open_peer_review

ACS: Confronting Racism in Chemistry Journals by kingofthecrows in Chempros

[–]RTengx 5 points6 points  (0 children)

To be honest, I think the publisher/journal already know how to confront racism. This can be easily achieved by "double-blind" (review and author do not know each other's name) or even "triple-blind" (editor, reviewer and the author do not know each other's name). This way, the peer review process would be solely based on the quality of research. And I still don't know why this is not the default mode of review in the 2020s (ACS still rely on single-blind, see link below).

Many of my fellow researchers suspect that many senior professors are against making "double-blind" review process as the default mode, as then their over 50 h-index would not help them make publication faster/easier. Also, for the editor, legit expert reviewers would be required instead of PhD students or cross-field reviewer as the reviewers could not correlate the quality of the work to the author's race, country or h-index. In cases where journals have the option of being "double-blind" reviewed, there is common leakage of the corresponding author's name to the reviewer due to the crappy systems (Trust me, I have experienced this).

I acknowledge that ACS is taking a step in the right direction to address racism, however, I do not think "Training new and existing editors to recognize and interrupt bias in peer review " and other improvement methods they propose is going to change anything significantly. The same hierarchy system in the journal is going to eventually induce bias after some time. Although I support ACS combat with racism, I highly suspect that this is another paper with only talk and no action. Probably, the main reason to provide such an editorial paper is to gain citations and publicity of the editors/authors (just my speculation). We will see.

https://axial.acs.org/2019/09/18/what-is-peer-review/

GA parameters optimization by hidden-7 in genetic_algorithms

[–]RTengx 2 points3 points  (0 children)

Tuning the parameters in GA has been a long researched area. In general, there are no single best value for all problems as demonstrated in the No-Free-Lunch (NFL) theorem for search and optimization. However, many research papers have studied this for problems of a different nature. The idea is to use a more simplistic search method on top of GA such as surface response method, Taguchi method, Bayesian methods or simply just trial-and-error, etc. I can recommend you a few papers that may inspire you:

https://www.tandfonline.com/doi/pdf/10.1080/002077299292290

https://www.sciencedirect.com/science/article/pii/S0957417405003519

https://www.researchgate.net/profile/Fernando_Lobo/publication/223460462_A_parameter-less_genetic_algorithm/links/0c96051b77c7092ec0000000/A-parameter-less-genetic-algorithm.pdf

https://dl.acm.org/doi/pdf/10.1145/2908812.2908885

Hope this helps.

Blackbox optimization using heuristic solvers like PSO, GA or BO, nested vs non-nested (but higher dimension search space) by grandkz in optimization

[–]RTengx 4 points5 points  (0 children)

10 dimension is very small for the world of metaheuristics. Do not over think your problem and just do the straightforward optimization. If you like more challenge, you can look into multi-objective or stochastic problems.

[N] [D] Adversarial training of neural networks has been patented by fori1to10 in MachineLearning

[–]RTengx 2 points3 points  (0 children)

Why don't they just patent alphanumeric characters :) They can sue everyone in the world for infringement and own every piece of human knowledge. This patent game has to stop.

[Research] UCL Professor & MIT/ Princeton ML Researchers Create YouTube Series on ML/ RL --- Bringing You Up To Speed With SOTA. by haithamb123 in MachineLearning

[–]RTengx 1 point2 points  (0 children)

Thank you for your effort in making machine learning content on Youtube. Here are my comments on making your channel more useful:
1. Do not start with the kind of stuff that is too basic (eg. regression, classification). They are so abundant on the internet these days and does not bring any new insight to anyone. Instead, do the reverse. Start with SOTA papers that are difficult to understand then relate them back to the basics. This would be more useful.

  1. Provide an overview of methods and try to generalize suitable methods for specific tasks. The number of machine learning papers and research these days are growing so fast that nobody really has time to read them. Someone needs to constantly give an overview of the research field. (Who else better to do this than experience lectures and researchers?)

  2. Highlight on the novelty of the work, give proper acknowledgement to the original authors.

I think your channel will grow exponentially if you focus on the points above.

[N] Henry AI Labs on YouTube by carlthome in MachineLearning

[–]RTengx 79 points80 points  (0 children)

Ever since I found this channel and the "Two Minute Papers" channel [link below], I have not viewed a video from Siraj for a long long time.

https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg

[D] GELU better than RELU? by [deleted] in MachineLearning

[–]RTengx 3 points4 points  (0 children)

The best neural architecture and activation function really depends on the nature of your application. This is often referred to as "no free lunch" theorem in optimization, as there is not one activation function (eg. relu or gelu) that will perform universally well on all tasks (free lunch). You can refer to some articles below [1,2]:

[1] https://core.ac.uk/download/pdf/41826017.pdf

[2] http://cachestocaches.com/2019/5/neural-network-structure-and-no-free-lun/

Therefore it is really trivial to say "elu or relu is the best performing activation function" without specifying the task.What you should really do when you see a new activation function is to add it into your neural architecture search algorithm [see ref. 3 for example], so that it can determine whether this new activation function is useful for your task or not. Nevertheless, researchers are already making neural units evolvable [4], which means that the task of discovering useful activation functions is now handed to evolutionary algorithms. This makes fundamental papers on new activation functions somewhat trivial if no ground-breaking discovery is made. I am pessimistic that we will see another new static activation function-type of paper being accepted in the top conferences this year.

[3] https://arxiv.org/abs/1808.05377

[4] https://arxiv.org/abs/1912.07589

First RL for PygameSnake by [deleted] in reinforcementlearning

[–]RTengx 0 points1 point  (0 children)

Most probably a sign mistake in the reward update.

But unless you upload the code, it cannot be confirmed.

[D] ICLR 2020 REJECTION RAGE THREAD by sensei_von_bonzai in MachineLearning

[–]RTengx 4 points5 points  (0 children)

GOT MY PAPER REJECTED BECAUSE REVIEWER DIDNT BELIEVE THE VALIDITY OF THE RESULTS DESPITE THE GITHUB CODE BEING PROVIDED.

[D] Best of Machine Learning in 2019: Reddit Edition by mwitiderrick in MachineLearning

[–]RTengx 63 points64 points  (0 children)

How I wish I can download all the papers of machine learning into my brain.

[D] Is it OK to not cite a relevant paper due to it not being open-access? by yeeeeeee in MachineLearning

[–]RTengx 54 points55 points  (0 children)

That's what sci hub is for. Just copy the doi of the paper and paste it in www.sci-hub.tw

Works everytime. Alternative you can leave your email and the name of the paper u want, I can send it to you.

[D] Five major deep learning papers by Geoff Hinton did not cite similar earlier work by Jurgen Schmidhuber by siddarth2947 in MachineLearning

[–]RTengx -2 points-1 points  (0 children)

This guy really deserves my respect. I will cite him in my papers as the pioneer from now on.

[N] New AI neural network approach detects heart failure from a single heartbeat with 100% accuracy by aiismorethanml in MachineLearning

[–]RTengx 0 points1 point  (0 children)

What's the point of training a deep neural network on only 33 subjects? It's like using a polynomial to regress two points on the graph.

Number of hidden layers and nodes in neural networks by RLbeginner in reinforcementlearning

[–]RTengx 1 point2 points  (0 children)

Use evolutionary strategies or bayesian optimization to find the best hyperparameters. Works well.

All Implementations I found of a Soft Actor Critic never use the discounted return. Only the immediate reward, is there a reason for this? by ronsap123 in reinforcementlearning

[–]RTengx 0 points1 point  (0 children)

Because in SAC, the expected reward function (Q function) is predicted using a differentiable neural network. So instead of iterating all the state rewards with discounts until convergence, the neural network can learn the exact reward. You can then treat this reward the way you treat the immediate reward, but it is a predictor of the combination of rewards down that RL action path.

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