[HN Gopher] MIT 6.S184: Introduction to Flow Matching and Diffus...
___________________________________________________________________
MIT 6.S184: Introduction to Flow Matching and Diffusion Models
Author : __rito__
Score : 374 points
Date : 2025-03-03 06:27 UTC (1 days ago)
(HTM) web link (diffusion.csail.mit.edu)
(TXT) w3m dump (diffusion.csail.mit.edu)
| __rito__ wrote:
| Our MIT class "6.S184: Introduction to Flow Matching and
| Diffusion Models" is now available on YouTube!
|
| We teach state-of-the-art generative AI algorithms for images,
| videos, proteins, etc. together with the mathematical tools to
| understand them.
|
| Flow and diffusion models are mathematically demanding subjects -
| which is why many lectures restrict themselves to teaching high
| level intuition. Here, we give a mathematically rigorous and
| self-contained introduction yet aimed at beginners in AI. We hope
| you will like it!
|
| From: https://x.com/peholderrieth
| kla-s wrote:
| Thank you, for making the effort of making this so accessible!
| Danke :)
| amelius wrote:
| Thanks!
|
| By the way, I was trying to go through the MIT Optics [1]
| course, but the audio/video quality is ... terrible. Could
| somebody fix that? (Maybe with diffusion models? ;)
|
| [1] https://ocw.mit.edu/courses/2-71-optics-
| spring-2009/resource...
| actualwitch wrote:
| Youtube link to playlist:
| https://www.youtube.com/watch?v=GCoP2w-Cqtg&list=PL57nT7tSGA...
| krosaen wrote:
| > Flow and diffusion models are mathematically demanding
| subjects - which is why many lectures restrict themselves to
| teaching high level intuition.
|
| I appreciate this - I hope norms develop to clearly identify
| whether learning materials / courses are about intuition or
| deeper applications that don't shy away from full
| prerequisites. They both have their place, but can be hard to
| find the latter amidst the sea of introductory materials that
| merely give intuition.
| lastdong wrote:
| Awasemoness! Since my undergrad days (it was a long time ago),
| I've found myself turning to MIT classes to help me grasp some
| challenging subjects. It's been such a great resource!
| whiplash451 wrote:
| Well done, folks. Congrats!
| Verlyn139 wrote:
| Nice
| jnkml wrote:
| This is exactly what I was looking for! Thanks for sharing
| ipnon wrote:
| Does anyone have a collection of all public courses on latest AI
| techniques?
| coolThingsFirst wrote:
| This
| esafak wrote:
| Just start an "awesome AI courses" repo on GitHub and invite
| PRs. Or update these:
|
| https://github.com/luspr/awesome-ml-courses
|
| https://github.com/owainlewis/awesome-artificial-intelligenc...
| schmorptron wrote:
| I'm incredibly grateful for MIT OCW and consorts. I've been using
| it as a secondary resource for my subjects and learning about the
| same topic in two different ways is incredibly helpful,
| especially hard to grasp ones.
| szvsw wrote:
| Conditional normalizing flows are one of the most beautiful
| solutions to inverse design problems that I've come across, if
| you have the data to train them. Something about the notion of
| carefully deforming a base distribution by pushing and pulling
| its probability mass around until it's in the right location by
| using bijective functions (which themselves have very clever
| constructions) is just so elegant...
|
| I've had some trickiness trying to get them to work when some of
| the targets are continuous and some categorical, but regardless
| just a really cool method... really nailed it on the name imo!
| coolThingsFirst wrote:
| Thank you so much, what other OCW courses exist on modern AI?
| fumeux_fume wrote:
| I'm so happy to find this here. LLMs seem to have diverted a lot
| of attention away from this incredibly useful technique.
| arolihas wrote:
| Cool course, can't wait to go through it! I noticed that this is
| focused strictly on continuous spaces, but there's a lot of cool
| stuff going on in discrete diffusion. Any plans for a follow up?
| I couldn't help but notice that the course teacher Peter just
| came out with a paper for discrete diffusion too.
|
| https://x.com/peholderrieth/status/1891846309952282661
|
| https://github.com/kuleshov-group/awesome-discrete-diffusion...
| whoisnnamdi wrote:
| Great for MIT to be putting out such timely and relevant content
| for free!
| bicepjai wrote:
| Thanks again. Past decade has been golden era for deep learning
| education. I love the fights of who will make high quality
| learning content free
| ddingus wrote:
| Would one of you, who is familiar with this topic, help me
| understand the primary use case(s) along with a few words, just
| your overall take on these techniques?
|
| Thanks and appreciated in advance.
| x1000 wrote:
| It's the fundamentals that underly Stable Diffusion, Dalle, and
| various other SOTA image generation models, video, and audio
| generation models. They've also started taking off in the field
| of robotics control [1]. These models are trained to
| incrementally nudge samples of pure noise onto the
| distributions of their training data. Because they're trained
| on noised versions of the training set, the models are able to
| better explore, navigate, and make use of the regions near the
| true data distribution in the denoising process. One of the
| biggest issues with GANs is a thing called "mode collapse" [2].
|
| [1] https://www.physicalintelligence.company/blog/pi0
|
| [2] https://en.wikipedia.org/wiki/Mode_collapse
___________________________________________________________________
(page generated 2025-03-04 23:02 UTC)