NewInML @ NeurIPS 2023 @NewInML
NewInML @ NeurIPS 2023 Check our website! newinml.github.io Joined October 2020-
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Poor neural networks... when they hallucinate, it's no good, and when they truthfully recreate ground truths, it's no good either 😂
It really grinds my gears when I hear professors imply that only high-risk research is valuable, or worse yet, that you should never do research that would happen anyway if you did not exist.
Proud organizers after a successful workshop day. Thanks everyone for joining!
Proud organizers after a successful workshop day. Thanks everyone for joining!
One of bests sessions I have ever attended!
One of bests sessions I have ever attended!
Happening right now: @_beenkim is sharing her strategy "winging it"! Join us to learn more about it
Our workshop for newcomers in ML is starting now! This morning we have inspiring keynotes (Hugo Larochelle now) and a panel discussion on *Slow Science* and more talks in the afternoon
Correction ‼️‼️ 11:30 - 12:30: Panel discussion 1:30 - 2:30: talk by Milind Tambe 2:30 - 3:30: Job Market talk 3:30 to 4:30 is the poster session
Correction ‼️‼️ 11:30 - 12:30: Panel discussion 1:30 - 2:30: talk by Milind Tambe 2:30 - 3:30: Job Market talk 3:30 to 4:30 is the poster session
I am grateful to have a helping hand in @pierrelux, @riashatislam and @pierthodo when I first started my research career as an MSc student in 2018. The advice they gave me on reading research papers, organizing my thoughts, and thinking like a researcher is still with me. (1/N)
Bastian Grossenbacher.. @Pseudomanifold
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Bastian Grossenbacher.. @Pseudomanifold
5K Followers 979 Following Dad · Neutral good · Topology & Geometry + ML · PI @HelmholtzMunich · Faculty @TU_Muenchen Feedback? https://t.co/w8J3u9u5JS
WiML @WiMLworkshop
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@MacGraeme42 @iamnafets interesting, thanks for the explanation
@giffmana Curious what you like about "mechanistic"? With "systematic" I'd have guessed more easily what they actually did in the paper. This seems like a way to make it sound trendy🤔 Like 5 years ago, when everything became adversarial
So I disagree with the bulk of the take. However, I wholeheartedly agree with the final conclusion, that "this is just engineering!" needs to die. The moment I hear someone say that phrase _in a condescending tone_, they are essentially dead to me.
When a student sadly tells me that the idea we've been working on for weeks was just arXived, I say: "Great! We've just gotten *strong* confirmation that our thinking was in the right direction. We've had the initial work done for us. Lets figure out how to make this 10x better"
Transformers strike again, where people thought CNN/UNet would prevail. (Sora too)
Announcing Stable Diffusion 3, our most capable text-to-image model, utilizing a diffusion transformer architecture for greatly improved performance in multi-subject prompts, image quality, and spelling abilities. Today, we are opening the waitlist for early preview. This phase…
i removed my earlier posts. i posted them because NSF's feedback hit me so hard this time from two directions; causality and healthcare. i continue to believe these two directions are really important .. but i'm a bit exhausted tonight.
The best performing hyperparameters are typically at the edge of stability -- so when you optimize neural network hyperparameters, you are contending with hyperparameter landscapes that look like this.
In both cases the function iteration can produce outputs that either diverge to infinity or remain happily bounded depending on those hyperparameters. Fractals are often defined by the boundary between hyperparameters where function iteration diverges or remains bounded.
There are similarities between the way in which many fractals are generated, and the way in which we train neural networks. Both involve repeatedly applying a function to its own output. In both cases, that function has hyperparameters that control its behavior.
Amazing finding (wonder how much the compute budget was :)). Not surprising if you think about how fractals emerge in even simpler settings.
The boundary between trainable and untrainable neural network hyperparameter configurations is *fractal*! And beautiful! Here is a grid search over a different pair of hyperparameters -- this time learning rate and the mean of the parameter initialization distribution.
"Pretty neat" how omitting (intentionally or not) an existing method can completely change the key takeaway of a figure. Good thing it's marked as "work still in progress" in the arxiv comment, so this should be easy to fix. Interesting to already have a domain name then 🤔
@_akhaliq I added OWL-ViT v2 to the plot. A single OWLv2 B/16 model, finetuned on O365+VG, covers all speed/accuracy combinations: Simply adjust the inference resolution to match your latency requirements. No re-training needed. arxiv.org/abs/2306.09683
Excellent work! Hoping to learn a ton! Also, it's nice to see celebrities like Will Smith contributing to open source research. Truly inspiring.
🔥Breaking News from Arena Google's Bard has just made a stunning leap, surpassing GPT-4 to the SECOND SPOT on the leaderboard! Big congrats to @Google for the remarkable achievement! The race is heating up like never before! Super excited to see what's next for Bard + Gemini…
Very interesting update. Maybe Google's distribution effects are starting to drive a runaway advantage...
🔥Breaking News from Arena Google's Bard has just made a stunning leap, surpassing GPT-4 to the SECOND SPOT on the leaderboard! Big congrats to @Google for the remarkable achievement! The race is heating up like never before! Super excited to see what's next for Bard + Gemini…
It can't be repeated enough: learning-rate is the single most bang-for-buck thing you can tune. If you think you know *ze best* learning-rate, it just means you only train standard stuff! This is not a "secret trick" either; it's stated very clearly in THE deep-learning book:
On the importance of tuning at least learning rate for your experiments!!! Here I just multiplied it by 10x and see the difference! For most of my models, the same value is close to optimal so initially I got lazy but for this captioning model, a much larger value was needed.
Has @ylecun become the new Lenna? ✨🤔
PhotoMaker by Tencent is out Customize photos within seconds, no LoRA training Demos - Realistic hf.co/spaces/Tencent… - Stylization huggihfngface.co/spaces/Tencent…
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