Видео

really cool shirt!

Tutorial de Kolmogorov Arnold networks en castellano: ruclips.net/video/Jb9wMCPUlnc/видео.html

So based on my understanding, this model's usability is hitched on the assumption that one has a "perfect" mapping function whereby no information is lost when applying Kolmogorov's theory to return the 1D edges? Because that in itself can be extremely difficult, even a near approximation.

Thank you a lot for the content! Great video!

Great job @AnneCarpenter

Can you explain why you used a Ca-Ca prior of 3.8 A for this model where you are denoising Cb coordinates?

What I am not able to fully explain/justify is the accuracy schedule. I understand the existence on an accuracy altogether, since we chose to work with distributions across the entire workflow. I speculate that increasing the accuracy has to do with the "smoothness"/stability of training; if we were to start with a very informative message that the sender transmits to the receiver, then the update step would he very large, propelling the theta parameters to high values. This is just my intuition. It's not explained in the paper

i am interested in this subject of drug discovery! fine

I’de like to join this lab as phD student

Very touch and informative

I didn't expect to se a mention to Jone polynomials...last time I talked about that was...well in the 80s.

This is super helpful! I have a molecular biology background, and this talk explained ML for molecular representation and scoring in a very accessible way.

I have been following Bharath for some time on his Discord channel. This talk makes me want to fly across continents to meet him in person and thank him for sharing his knowledge with the community.

cool

thank you

Thanks a lot! Based on my current understanding I would say quickest starting mental image for me to understand BFN's would be telling me that it is a standard NN with Bayesian teacher forcing.

This Hannes Stärk guys is a legend for organizing all this

How can i join

How can we join research position in the lab?

Thanks, Boyuan! Really interesting work!

interesting topic!

Mathew sounds almost identical to the biologist Mike Levin from Tufts

Can we use this for time series to forecast the future value ?

Thank you for making this, it will be better if there’s less ad. There’s at least 4-5 through this video

Great conversation, thank you!

Could you elaborate more on how the allegro model allows for more parallelization? You say that there is no message-passing, but then indicate that there are layers for tensor products. I’m curious what the layers for tensor products are for if there isn’t message-passing?

Great research, thank you for the presentation!

Great video

Great video

Thank you!

the formula at screen 's right corner , can't read

cool

Interesting.......Thanks.

All of these posts are solid platinum. Keep it all coming!

Martin is having a lot of fun in the background 😂

I really enjoyed this, strong generalisation! The previous papers on bias free CNNs are also really good, there's a few good talks given by Eero Simoncelli on youtube. Do you think you could do a talk on some follow up papers? It feels like something in this space might be a "Deep Image Prior" moment for 2024! That's cited 3000+ times now.

Very useful ❤ and nice explanation @Nicholas Gao

vícios de linguagem e um raciocínio descontínuo.

Why am i seeing this?

Was waiting for this thankss a lot!!

Starts at 56:34

Thanks for listening! Slides and links to the paper/code/etc are on my website!

John Snow?

Thank you very much for your video. I still have some doubts, is the KAN network suitable for multiple outputs?

Thank you for the discussion.

Thank you for uploading!!

Hey, where can I find the animation at @00:28 ?

One question I have is, are these flows monotonically increasing? The usual "block" flows have this nice property, but do these continuous flows trained with flow matching also have this property in the transformations from base to data?

Are we talking here about a general representation theory? Are b-splines the only basis set that can be used? What about wavelets, Fourier series, etc.?

exactly 10% of your subs liked