Tim

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Tim

Tim

@daidailoh

Dr. - Freelance AI Dev/Researcher - LLMs, VLMs, CV - ex RWTH Aachen - explaining science for @golem - Cosplay events at Sewcase e.V. - GameDev on full moon

Aachen, Germany Katılım Kasım 2015
448 Takip Edilen127 Takipçiler
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Tim
Tim@daidailoh·
Because I got nothing better to do, I started a github repo for super clean / super simple examples of modern deep learning networks, like GANs, VQ-VAEs, etc. - 99% of pytorch users should have everything installed already. Soon(tm): Diffusion, PointNet github.com/DaiDaiLoh/Exem…
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Tim@daidailoh·
@thegautamkamath @kgorman Love it that you actually followed through :) Side question: Is there any way to get informed when the list of accepted workshops are out / any date for that...?
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Gautam Kamath
Gautam Kamath@thegautamkamath·
As co-comms chair of ICML 2026 (w @kgorman), I'm super proud of how transparent we've been able to be on all of the (bold!) decisions made. Thanks to all the organizers (esp PC chairs) for being aligned on this. The community deserves to understand these important decisions
ICML Conference@icmlconf

To ensure compliance w peer-review policies, ICML has removed 795 reviews (1% of total) by reviewers who used LLMs when they explicitly agreed to not. Consequently, 497 papers (2% of all submissions) of these (reciprocal) reviewers have been desk rejected Details in blog post 👇

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Tim
Tim@daidailoh·
@norpadon Couldn't resist :D (no hard feelings, I'm sure that research is pretty cool to someone somewhere outthere ;P)
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Tim
Tim@daidailoh·
@giffmana @xiangyuqi_pton The RWTH/VCI special ;) Best way to learn, sadly also the best way to feed one's own anxiety to not graduate...
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Tim
Tim@daidailoh·
@_akhaliq Just tried it on the plot of my "Multidimensional Byte Pair Encoding"-Paper... Fucking lit! Next Paper is going to be a banger! arxiv.org/abs/2411.10281
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Tim
Tim@daidailoh·
@lossfunk @bitspilaniindia I'm so ready for agents complaining on moltbook that their papers were rejected because a human wrote their assigned reviews for them!
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Lossfunk
Lossfunk@lossfunk·
📢 Announcing CAISc 2026 - a new academic conference where AI systems are the primary authors and reviewers of scientific papers. Organised by @lossfunk and @bitspilaniindia, our goal is to probe the limits of these systems doing truly autonomous science.
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Tim
Tim@daidailoh·
@CSProfKGD I'm so ready for agents complaining on moltbook that their papers were rejected because a human wrote their reviews for them!
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Gautam Kamath
Gautam Kamath@thegautamkamath·
@ziv_ravid 1. This policy was only applied to people who agreed not to use LLMs for their reviews, but then used LLMs anyways. It's not an anti-LLM policy, it's a rule-following policy. 2. More sophisticated methods were used than AI detectors. Post from @icmlconf tomorrow.
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Ravid Shwartz Ziv
Ravid Shwartz Ziv@ziv_ravid·
I (still) wasn't affected by the ICML review policy, which desk rejected all the papers of reviewers who used LLMs to write their reviews (and didn't explicitly mention it) 😱, but this is a bad decision and not a good way to handle AI reviews. First, AI detectors are not reliable enough, with many false positives. Second, if it's a good review, why should I care that AI wrote it? We're using AI assistants everywhere in our day-to-day lives. What is the next step? To ban AI coding agents? I understand the motivation to prevent low-quality reviews, but this is not the way to improve them
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Tim
Tim@daidailoh·
@giffmana been out of the image generation game for a bit, but my guess is: all big models to some form of RL with some VLM as a judge that says "too many fingers" to iron out the last few kinks, plus vastly more data?
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Lucas Beyer (bl16)
Lucas Beyer (bl16)@giffmana·
I have a question about last year's image-generation progress, wonder what y'all think. How did we go from all models consistently getting fingers wrong, to all models consistently getting them right? This "flip" seems to have happened basically across all companies/models at the ~same time. Even "random" non-frontier papers seem to get it right? Or they just cherry-pick the figures?
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Tim
Tim@daidailoh·
@Markus_Soeder "Wir brauchen in Deutschland weiterhin High-Tech Pferdekutschen! Innovation statt Idoologie, damit Arbeitsplätze und Wertschöpfung erhalten bleiben!"
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Markus Söder
Markus Söder@Markus_Soeder·
Wir brauchen in Deutschland weiterhin einen Hightech-Verbrenner! Innovation statt Ideologie, damit Arbeitsplätze und Wertschöpfung erhalten bleiben.
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Tim@daidailoh·
@CSProfKGD So instead of 1 person standing around, it's now 5-6?
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Tim@daidailoh·
@giffmana "Untuned Hyperparameters Are All You Need"
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Lucas Beyer (bl16)
Lucas Beyer (bl16)@giffmana·
New optimizer with earth-shattering plots making the rounds, and published in Nature too (Machine Intelligence, but let's just drop that part.) So of course I had to take a quick look. A few things I noticed that make me a bit sus, though I'm not saying to outright discard it - Each point is caption of the corresponding screenshot below: 1. What on earth are these SGDM vs AdamW gaps? They are not normal -> untuned baselines? (Also: what good is a Nature MI editor, if they approve plots with "0M" everywhere on x-axis???) 2. For vision models they tune lr's, good. But not wd or other optim hparams, meh/sus. 3. For LLM, they select hparam on test. At least epochs, but given this and that they seem to use "validation" and "testing" as synonyms in the paper, probably everything. 4. I am not sure a Medium blogpost tutorial with an arbitrary hparam selection is a good starting point for the baseline of a Nature MI paper?? Maybe this new optimizer is as amazing as promised, but I'll need to see less suspicious evidence. I wish the reviewers had asked for that. Maybe someone put it to test on the nanogpt speedrun? At least that has heavily-tuned baselines, including optimizers.
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Ji-Ha@Ji_Ha_Kim

Woah, how did I never hear of this? An optimizer paper that got published in Nature, looks quite substantial

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Tim
Tim@daidailoh·
@giffmana It's sometimes breathtaking how much bullshit gets published in nature, especially for cross-domain stuff like ML in medicine... My favourite I've encountered so far: Training and Testing on the same dataset.
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Tim
Tim@daidailoh·
@CSProfKGD IMHO they should select oral talks on presentation ability. Maybe pre-select a few, then have people review a 1-minute-version video of the talk or something? I'd rather hear someone explain a mediocre paper well rather than watch some guy mumbling for 15 minutes about tables...
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Tim
Tim@daidailoh·
@sang_yun_lee Yes please, I'm sick of all the diffusion stuff that gets more complicated and bloated by the minute :D
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Sangyun Lee
Sangyun Lee@sang_yun_lee·
Are generative autoencoders coming back?
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Tim
Tim@daidailoh·
@prajdabre If you want to be a smartass, you can say "you didn't specify where" and go on to explain sigmoid-based attention as that e.g. eliminates attention sinks
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Raj Dabre
Raj Dabre@prajdabre·
Basic ML question: Interviewer asks: Can I replace the softmax function with the sigmoid function since both functions cause values to be between 0 and 1? You say yes and fail the interview. Why?
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Tim
Tim@daidailoh·
@daseyb Control over (temporal) information density = key to success :)
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Dario Seyb
Dario Seyb@daseyb·
Why waste compute on areas of the image that are supposed to stay the same? In EditCtrl we allocate compute to where it's needed, significantly speeding up video diffusion based inpainting!
Yehonathan Litman@yehonation

Excited to share our new work EditCtrl! We introduce a disentangled local-global control video inpainting framework that dynamically allocates compute where needed - achieving up to 10x compute savings over full-attention while matching or exceeding SOTA editing quality. 🧵

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Tim
Tim@daidailoh·
@jxmnop (Proudly made the image edit with ChatGPT^^)
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Tim
Tim@daidailoh·
@guy_dar1 @SwayStar123 Fuck being polite and nice, that gets you nowhere. At least this feels satisfying for a minute^^ make people accountable publically, everything else won't do - those supervises probably would've had a good laugh about discussing the issue with them at best...
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Guy Dar
Guy Dar@guy_dar1·
@SwayStar123 You don't need to be nefarious to not like being quite frankly dunked on (it's not that uncommon that a student does all the work). If you have grievances with your supervisors, even legitimate ones, maybe it's best not to publish them to the world.
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Tim
Tim@daidailoh·
@NielsRogge Yup, same. Yet if you're a PhD student and desperately need to get a publication, I totally get it...
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Niels Rogge
Niels Rogge@NielsRogge·
Papers like these are so boring It's a bit lame that as a researcher, rather than exploring new ways of doing vision with neural networks, they chose the comfortable path of just tweaking some things here and there to ViTs, leading to a pointless 84% accuracy on ImageNet, which people already achieved 3 years ago, with an architectre that is still very limited to count things reliably in an image for example
Tanishq Mathew Abraham, Ph.D.@iScienceLuvr

ViT-5: Vision Transformers for The Mid-2020s "a systematic investigation into modernizing Vision Transformer backbones by leveraging architectural advancements from the past five years" * LayerScale * RMSNorm * original MLP design with GeLU activation * both APE and 2D RoPE jointly * registers with a separate 2D RoPE * QK-Norm * remove bias terms in the QKV projection layers 84.2% top1 accuracy on ImageNet-1k, 1.84 FID on ImageNet-256

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Tim
Tim@daidailoh·
@CSProfKGD BUt tHe oNE SLiDE PeR MInUtE rULe
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Kosta Derpanis (sabbatical in Munich 🇩🇪)
#KostasThoughts: Like many others, I used to cram too many images or videos onto one slide. They end up too small, the audience doesn’t know where to look, and you rush through them. My rule now: one idea or example per slide. Slides are free, use them. Here's a comparison: (left) cramming examples, and (right) letting each example breathe on its own slide. Which do you prefer?
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