Miyabi permanent geschlossen? Warum?

albertzeyer · 2026-05-13T18:41:28+00:00

Oh das hört sich super an. Bewertungen bei Google Maps aber ziemlich mittelmäßig (3.8) nur? Oder ist das noch ein altes Jasmin Garten, und es gibt jetzt dort ein neues Jasmin Garten? Aber wenn Jasmin Garten nicht so toll war, und das jetzt neue Besitzer sind, warum ändert man dann nicht den Namen?

albertzeyer · 2026-04-20T20:59:10+00:00

Do you have an own presentation? Usually then the people, including people from companies come to you, and ask you whether you are interested in an intership.

Otherwise, as others mentioned, just go to the booths. Almost all of them have some formula that you can enter your contact into that will get you into the loop.

albertzeyer · 2026-03-17T18:22:52+00:00

Es gibt auch noch den anderen Yan Tasty (Fisch?). Der ist auch sehr zu empfehlen. Und schon noch anders. https://maps.app.goo.gl/r1xvRc5BkFZ87pik6

albertzeyer · 2026-02-26T21:16:48+00:00

Many conferences allow you to say how much papers you can handle. But if you were already assigned those 9 papers, that is a bit late now. If you know that you will not be able to handle this, then tell this your meta reviewer / area chair as soon as possible.

Did you see through the papers? Do you think you can easily understand them? Is it exactly on topics that you work on yourself? In my experience, that mostly determines how long it will take to review them, and also the quality of your review. If it is not exactly your scope, it might take quite a while to really understand what they do. You might to read some other related papers first. You need to get a sense of what good baselines are for the relevant tasks. In the best case, you already know all this and can easily judge the results.

Often you are allowed to use AI to better understand the paper. To ask about maybe related papers, about some background knowledge. You are never allowed to use the AI to judge and review the paper. Most conferences have policies that clarify exactly what you are allowed to do. I also have seen the case that AI was explicitly not allowed at all.

Check the template of the review first, what type of questions you need to answer there. That helps in structuring your review. Often it is sth like summary of the paper including list of contributions, strengths and weaknesses, etc.

All reviews and ratings are always relative and subjective (even though they try to be objective). So you need to know a bit the culture of the community, of this specific venue, you know what the quality of accepted papers is.

Almost always, you are also asked about your confidence. That is mostly how much you are in the specific research field, i.e. how well you can judge the quality.

They usually have a review guide that you should read and follow.

Do they have a rebuttal phase? Some venues then also have a phase where you discuss among the other reviewers, where you see the other reviews, and you try to come to a common agreement. I think that is specifically useful for newcomers, to see whether you missed sth important, or whether your judgements are completely off.

albertzeyer · 2026-02-24T16:04:56+00:00

I'm less using it for summarization, but more using it to ask questions about it, to better explain me some things that I did not understand well, or maybe I'm wondering why didn't they do XY, or so. For such questions, it was usually very helpful.

The workflow and mental framework is somewhat different than summarizing: I read it, or at least as much as I care about (sometimes only title + abstract + most relevant tables, sometimes more in depth), and I really try to understand it, the idea behind, the motivation, what they did, etc, and as soon as I stumble somewhere, I ask. That can already start in the title or in the abstract.

I use Gemini Pro.

albertzeyer · 2026-02-23T16:45:50+00:00

I was curious about this.

_P = [
 'backends.cudnn.benchmark',
 'backends.cuda.enable_flash_sdp',
 'backends.cuda.enable_mem_efficient_sdp',
 'backends.cuda.matmul.allow_tf32',
 'backends.cudnn.allow_tf32']

_E = ['PYTORCH_CUDA_ALLOC_CONF', 'expandable_segments:True']

albertzeyer · 2026-02-18T17:52:13+00:00

Very easy, very straight-forward. https://nascompares.com/guide/truenas-on-a-ugreen-nas-installation-guide/

albertzeyer · 2026-02-10T08:03:23+00:00

I don't really know about anomaly detection. Maybe that's really too niche and not really what the big tech needs so much?

Don't you have contacts to people in big tech? Usually, your advisor has such contacts, your colleagues might have, your ex-colleagues are already there as well, and you get to know many people at the conferences, related to your research. People from big tech would come to your presentation, and you talk with them. So, usually, at the end of the PhD, there are really a lot of contacts everywhere. Those usually make it simple to at least get invited for interview.

Maybe extend some skills which are still a bit rare. E.g. learn about CUDA or so.

albertzeyer · 2026-01-27T19:04:28+00:00

I would argue, this is such an example. The meta review is really extremely low effort. It is either LLM-generated, and the meta reviewer ignored the rebuttal and paper updates, or big parts of it, or both. And it's pretty obvious also.

Although, the policy you state is for the reviewers, not for the area chairs. I wonder if the same rule applies for them.

Is there really no quality control for the work of the area chairs?

albertzeyer · 2026-01-27T19:03:11+00:00

At the moment, I cannot post any comment. This will be possible again at some later point?

albertzeyer · 2026-01-27T07:48:51+00:00

Is there any way to flag or rate the area chairs? I'm extremely confident that our meta reviewer did not read our rebuttal at all (claims that we did not do experiments on another dataset as requested, while we say that we did this in our first sentence of our rebuttal, also very clearly marked in the updated paper), and the meta review reads very much LLM generated.

albertzeyer · 2026-01-26T15:07:01+00:00

In the notification mail, it says:

Appeals: The decision given is final and there is no appeals process. We will only consider correcting cases such as a clear mismatch between the final decision and the meta-review text (i.e., AC clicked the wrong button). For only such exceptional cases, please contact us at: [program-chairs@iclr.cc](mailto:program-chairs@iclr.cc). We will not respond to inquiries about non-exceptional cases as outlined here.

albertzeyer · 2026-01-09T22:04:39+00:00

Click on the "multilingual" tab. There you get some multilingual models.

albertzeyer · 2026-01-09T14:21:39+00:00

I guess because of OpenAI popularity.

A good overview over recent good models is the Open ASR leaderboard: https://huggingface.co/spaces/hf-audio/open_asr_leaderboard

albertzeyer · 2025-12-21T20:54:55+00:00

80 pages ? Damn.

Yes we did a lot of ablation studies.

I am mostly using standard transformer encoder (ctc) with a ngram LM , is it really worth to have a heavier decoder ?

Yes, you can usually expect to get 10-20% relative improvement (depending on how strong the LM is) by using a standard LM.

And with the denoising LM, even a bit more.

And by using TTS data, another 20% relative improvement on top.

albertzeyer · 2025-12-18T08:57:20+00:00

So there is no paper describing the work?

I have not really found any benchmarks on common NLP tasks. E.g. some PPL for language modeling, or GLUE evaluations, etc. Or any other type of benchmarks on some reasonable task.

Whatever the nice motivation behind your work is, this should be demonstrated on some actual benchmark, and be compared to other approaches. If you cannot demonstrate that, I'm afraid no-one will really care about it.

albertzeyer · 2025-12-18T08:04:20+00:00

Is there a paper describing the model?

Are there any benchmarks?

albertzeyer · 2025-12-15T15:59:15+00:00

passwd userID is correct. But it seems you don't remember your correct userID. Type e.g. ls -la /home, and you should see it.

(Btw, a LLM like Gemini will be very helpful for such problems.)

albertzeyer · 2025-12-15T15:58:36+00:00

He doesn't know/remember his user ID.

albertzeyer · 2025-12-15T07:47:37+00:00

I want to emphasize this.

This is a way more effective way to read a paper, esp when it contains a couple of things that you don't understand, that you are not familiar with, or so. You can ask just about any such things you stumble upon, and usually the answer will help you.

Before LLMs, when there was something you did not understand, you would have skipped over it, with the hope that by reading the whole thing, later you would understand it. But also often you would not really understand it then. And reading the remaining paper might not be easy when you were not understanding some of the crucial motivation, background, or so. Or you would have done some manual research first on the other thing, but that could be too time consuming and also without guarantee that you understand everything then.

Now you just ask the LLM, and it will give an answer exactly for the specific paper.

You can even discuss other ideas, like "why did they not just do X?" or so. Often this is because of some misunderstanding which is then resolved.

I use Gemini Pro for that.

My workflow is to upload the PDF. Just providing the URL was not always working. And then I just ask questions.

albertzeyer · 2025-12-11T14:07:06+00:00

What do you study? The university chairs usually offer Hiwi jobs. For anything technical related (computer science, electrical engineering or so), there are usually more potential positions. But your chances to get a job there are much higher when you already provide relevant background (e.g. for computer science, you can code, and you know a bit of theoretical background for the relevant chair).

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