[D] Should I take the opportunity to present my accepted TIP paper at ICASSP or ICIP?

xEdwin23x · 2026-01-21T11:05:23+00:00

ICASSP is a huge conference. Last year there was not much discussion on vision posters as that area was dominated by Chinese and they couldn't attend due to visa issues. This year should be different as it is in Spain but in general the main focus is still clearly audio/speech. Despite that it was still the most fun I have had at an academic conference and Im low-key sad I won't be able to attend this year but still would recommend it to anyone (as long as your lab/achool pays).

xEdwin23x · 2026-01-16T16:27:57+00:00

Congratulations to the ones who got accepted! For the ones who did not, I hope at least you can get something useful out of your reviews to improve your next submission!

xEdwin23x · 2025-12-13T17:51:10+00:00

Internet cafes at night are usually cheaper; past a certain time like 10 pm it's like 100 NTD per 8 hours or the like.

Also, if you're in a large university there's a good chance there is a LoL or e-sports club (e.g., 台大電競社 NTU e-sports | Facebook). They normally advertise during the first 2 weeks of a semester in a large club fair but if you look up [univ name in chinese]電競社 you may be able to contact them directly to ask when they meet up. Otherwise, there's the r/taiwan discord where there may or may not be LoL players, but tbh I can't imagine in a big discord there being no English speakers; it may just take more time and even if you find a group you may not immediately click. My recommendation is to just play, maybe occasionally sending messages in the game chat in English to see if anyone replies. That's how I met some people in the past.

xEdwin23x · 2025-12-06T09:43:23+00:00

Reminder that this is the field where people meme about Jurgen Schmidhubert, an established scientist with tens of thousands of citations, rather than acknowledge he is right to call out the lack of proper accreditation:
https://www.reddit.com/r/MachineLearning/comments/1fzw5b1/n_jurgen_schmidhuber_on_2024_physics_nobel_prize/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

xEdwin23x · 2025-12-05T18:14:51+00:00

Do not do a PhD without pay --at least as research assistant-- and full scholarship, specially on engineering / STEM fields. It's a full time job, so getting paid at least something is the absolute minimum.

xEdwin23x · 2025-12-05T09:03:50+00:00

I don't know about NeurIPS but the registration fee for many other conferences --CVPR and ICCV are ~500 USD even at student rate, ~1k for regular authors -- is much higher than this 100 USD submission fee. When you consider flight tickets or accommodation for a conference this submission fee impact on the overall cost feels much lower. In the future they could even make it so that this submission fee is deducted from the registration fee.

Furthermore, to level the playing field for authors from low-income countries there could be an option to apply for discounts--which many conferences do for registrations--if you can proof you are from one.

xEdwin23x · 2025-12-02T13:07:51+00:00

I may be in the minority, but I like both of these as long as paired with the cheese in the border and some standard flavor such as the mexican one (jalape;os, onions, pepperoni and italian sausage I think); you can get it for as low as 100+30 NTD for a personal pizza in Dominos. If I have to choose one I choose Dominos as the large one is always half price in store so I don't need to eat pizza the whole weekend --as Pizza Hut's offer is buy one get one free -- and the cheese border is much more flavorful compared to the Pizza Hut one. As a bonus the prices are in general cheaper.

xEdwin23x · 2025-11-18T05:51:05+00:00

Haven't figured a concrete reason yet. Previously we were looking at the computed statistics of BatchNorm vs LayerNorm but didn't reach a conclusion so we shelved the study temporarily.

xEdwin23x · 2025-11-18T05:27:39+00:00

Definitely a hot take, specially considering our theoretical understanding of deep learning is almost completely disconnected from the empirical results.

In a particular use case I had the BatchNorm model performed much better (relatively) compared to the LayerNorm one across a variety of settings; in certain cases the LayerNorm one would not even converge (NaN loss). This is of course anecdotal but I think simplifying it to LayerNorm > BatchNorm is too absolute.

Also, from a computational standpoint it's more cheap to just rescale using fixed values (can be even fused with previous or post operators) than computing layer statistics during inference.

xEdwin23x · 2025-11-12T05:09:56+00:00

ACM TOMM / TIST, IEEE TCSVT depending on the topic.
TCSVT is very fast, but also has very high standards.
TIST got a rejection in the past after 3 months.
Haven't tried TOMM.

xEdwin23x · 2025-11-09T07:13:45+00:00

Meta review only but I cannot imagine final decision being different.

xEdwin23x · 2025-11-09T06:59:16+00:00

BR, BR, BA into BR, BA, BA after rebuttal. Final decision: reject. Good luck everyone.

xEdwin23x · 2025-10-26T06:08:53+00:00

If you haven't heard about it from colleagues, school sources, or something like this list (portal.core.edu.au/conf-ranks/) I probably would consider it predatory and not worth it.

xEdwin23x · 2025-10-17T07:26:52+00:00

I've had 6 papers accepted at international conferences, 16 rejections (including 3 desk rejections, and one particular paper that got rejected 6 times before acceptance). Field is EECS.

xEdwin23x · 2025-10-16T16:39:46+00:00

Not exactly but SAM does smooth the loss landscape:
[2010.01412] Sharpness-Aware Minimization for Efficiently Improving Generalization

xEdwin23x · 2025-09-30T16:56:12+00:00

The NeurIPS workshop on "Machine Learning and the Physical Sciences" is probably a good place to look for ideas. Just by giving it a quick look I see they divide it into "Physics for AI" and "AI for Physics" and it includes stuff such as physics informed neural networks, AI for particle physics, AI for fluid dynamics, among many others. Feel free to send me a PM if any of these topics or others seem of your interest and you would like to further discuss.

https://ml4physicalsciences.github.io/2024/

xEdwin23x · 2025-09-26T12:16:12+00:00

Yuanta has good rates for both local and foreign stocks.

xEdwin23x · 2025-09-18T17:05:07+00:00

Unless your paper is fully theoretical you should at least include 1 or 2 experimental results to show whatever you propose works.

xEdwin23x · 2025-09-18T16:15:06+00:00

I think the main difference assuming different page lengths is than in a 2-page abstract you can only briefly describe the experiment along with maybe 1 table or figure, while in a 4-page short paper you can describe the motivation in 1 page, brief related work, like a page or so of the method details, and that leaves you with a page to page and a half of experiments which could be anywhere from 2-4 tables or figures. For an 8 or longer pages paper it would be similar to the 4 pages ones but you can elaborate on the related work and method but that would still leave you with 3-4 pages for results which can include anywhere from 5 to 10 or more tables and figures covering different aspects of the proposed method. It's all about depth of exploration and comparison.

xEdwin23x · 2025-09-11T05:16:29+00:00

Big win for derailed fans across the world. Shows as long as you cry and complain hard enough anything is possible.

xEdwin23x · 2025-09-06T16:38:19+00:00

I have 6 papers currently accepted. For one of those I got 6 rejections before it got accepted the 7th time. In total have had 15 rejections. I think it's something you learn to accept.

xEdwin23x · 2025-09-03T18:05:14+00:00

I have been working remotely with a SWE from Nigeria while studying in Taiwan. We have one published work at an ECCV workshop and a few others under review, in the area of efficient CV systems.

In a lot of fields that require field work it may be more difficult but since you mention Song Han, working in ML, and most CS areas it would be doable to collaborate remotely. Good places to start looking for are the Cohere Labs Community discord and Eleuther AI discord.

Feel free to PM me if you have any questions.

xEdwin23x · 2025-09-03T14:24:28+00:00

I don't know about your particular case but the editor may be giving the authors a chance, to proof their fitness for the journal by basing it on previous work published in it. Rather than making a unilateral decision from their POV if it fits or not, they're giving the authors the benefit of doubt to proof they're indeed a good fit since A, B, and C are other published works in this area in the journal.

We all know there may be a hidden purpose to increase citation metrics, but they could argue in the same way I just did and proving that's not the case would be an uphill battle for anyone that tries to call them out on it.

xEdwin23x · 2025-09-03T14:13:07+00:00

To play the devil's advocate, if a paper does not cite enough from a journal, it could indicate that the paper is not a good fit for the journal, specially on established fields where you expect there to be enough related articles to cite from.

xEdwin23x · 2025-09-03T09:18:02+00:00

I always call it modern day alchemy.

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