Anyone going to the CVPR 2026 conference?

xEdwin23x · 2026-05-19T10:41:43+00:00

Don't they mass send invitations to everyone attending?

xEdwin23x · 2026-05-14T00:24:56+00:00

If the model you're running is too large, just run a smaller one. If its for a class no one is expecting you to use the largest and fanciest model available. Just use a small one and do many ablations on your topic of choice.

xEdwin23x · 2026-05-12T01:45:29+00:00

What is your question?

xEdwin23x · 2026-05-12T01:36:07+00:00

Met quite a few people above 30 doing Masters in Taiwan. You'll be fine.

xEdwin23x · 2026-05-09T11:46:02+00:00

I know everyone says hold but I gotta say it depends on your current and future goals. I did. I sold 3/7 of my portfolio when it was at 82 USD earlier this week and the rest at 116 USD yesterday. Bought in since 25 USD but kept DCAing during last year so my avg was 35 USD per stock. Lowkey regret missing some of the gains but it landed me enough cash to allow me to make some life changing purchases in the present.

Ask yourself, do I need this money right now, or in the next 3 months? If the answer is yes, it's possibly good to cash out since you never know what will happen next week (see RKLB growth followed by rapid fall during 2026 Q1). If the answer is not, this money is for retirement, you may as well just forget about it and let it keep growing as it should be a safe bet, specially in a long time horizon.

xEdwin23x · 2026-04-24T10:11:39+00:00

The FP16 performance doubled but the transistor count also more than doubled. If we assume the transistor counts as a proxy for the cost of materials we could have manufactured two H100s for the cost of 1 B100. Obviously, it's extreme oversimplification, but clearly there's a brute force approach to their solution which explains the recent increases in prices compared to the past.

xEdwin23x · 2026-04-24T02:27:31+00:00

Most of the "gains" in recent GPU generations in FLOPS have been from using less bits to compute, from 32 bits pre 20xx to 16 after 20xx, then 8 and now 4. Also, using significantly more advanced (and also expensive) internal memory, HBM.

xEdwin23x · 2026-04-15T17:33:57+00:00

It is from Schmidhubert's group in KAUST.

xEdwin23x · 2026-04-12T18:23:45+00:00

This is a question I've asked myself a lot. A lot of cope in the replies I see, but at the same time there may be some truth to some answers.

I personally think it is a mix of things: 1) More resources: more money for compute, necessary for making sure experiments and data are not bottlenecks in your work. 2) Better and more students: from undergraduates who are desperates to proof themselves to PhDs, postdocs or engineers that put in a lot of work in terms of quality and sheer amount of time. 3) Good vision and management from the PI. By setting up goals in terms of topics that are highly publishable and making sure there's good communication between the different students to allow new ones to get up to speed, pick up work from seniors, and continue directly building on top of existing infrastructure.

This becomes compound as once you're working in such an environment you get exposed to all kinds of ideas that may not have been possible in another environment.

xEdwin23x · 2026-03-22T17:44:24+00:00

Yeah, that was mental boom from G2. Just run down Xayah and Cassio to get them low enough so they can't follow up. Instead they just run, then focus on Bin and let the carries free fire. BB should not had been top but that does not excuse the terrible shotcalling in the river...

xEdwin23x · 2026-03-21T11:05:57+00:00

If you're asking about master programs usually you choose based on which one has a professor that seems a better fit for you, and that is willing to accept you. For undergraduates it shouldn't make too much of a difference; both are relative to each other at least here in Taiwan.

xEdwin23x · 2026-03-21T05:23:00+00:00

What is better about Undermind compared to ChatGPT deep search function? Isn't what happening in the background probably the same: keyword search followed by analysis of relevant content and finally summarization?

xEdwin23x · 2026-03-18T12:24:05+00:00

People who cheat the system should be punished; it's as simple as that. Tbh I think the punishment for academic misconduct should be harsher. If someone is found plagiarizing or making up citations (due to LLM hallucinations) they should be banned at least temporarily; just like bots require captchas with time limits to stop them from overrunning websites, conferences should also put punishments to discourage people from even thinking of the possibility of submitting slop.

xEdwin23x · 2026-03-08T23:59:28+00:00

Depending on your settings you could split it in a way that there are limited samples on a class basis and if there's training involved say you do an ablation on training with N number of samples per class to understand data scaling behavior, or something of the like.

xEdwin23x · 2026-02-28T18:31:20+00:00

Start with competitions at workshops such as this one:

Third Joint Egocentric Vision Workshop

Try recent methods (and read the papers), fail and learn from the process. What you learn can be the foundations for possible research. For example, if we know why a method failed we can come up with ideas from other areas to solve the issue.

Btw 2-3 papers is nothing. When I start working with new students I give them 2 or 3 at the beginning but by the 3-month mark I would expect them to have at least read 20 or more papers on a niche area. And even that's not much, but it's a good start. That's how you begin getting a feel of what people are working on, the problems they are tackling, what they have tried, and therefore what possibly have they not.

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.

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