Samuel Ainsworth is in NYC Profile picture
Sep 13, 2022 β€’ 13 tweets β€’ 5 min read β€’ Read on X
πŸ“œπŸš¨πŸ“œπŸš¨
NN loss landscapes are full of permutation symmetries, ie. swap any 2 units in a hidden layer. What does this mean for SGD? Is this practically useful?

For the past 5 yrs these Qs have fascinated me. Today, I am ready to announce "Git Re-Basin"!

arxiv.org/abs/2209.04836
We show that NN loss landscapes contain effectively only a single basin(!) provided sufficient width. Even better, we develop practical algos to navigate these basins...
Say you train Model A.

Independently, your friend trains Model B, possibly on different data.

With Git Re-Basin, you can merge models A+B in weight space at _no cost to the loss_
Git Re-Basin applies to any NN arch & we provide the first-ever demonstration of zero-barrier linear mode connectivity between two independently trained (no pre-training!) ResNets.

Put simply: a ResNet loss landscape contains only a single basin & we have algo to prove it
Phenomenon #1: "merge-ability" is an emergent property of SGD training -> merging at init doesn't work but a phase transition occurs such that it becomes possible over time
Phenomenon #2: Model width is intimately related to merge-ability: the wider the better. Not too burdensome of a constraint since we're all training in the wide/overparameterized regime anyways. Important nonetheless...
Also, not all arch's are equally mergeable: VGGs seem to be harder than ResNets πŸ€·β€β™‚οΈ We hypothesize that merge-ability is an indicator of compatible data/arch fit.
Finally, my fav result: it's possible to train models on disjoint and biased datasets, then merge them together in weight space.

Eg, you have some data in US, some in EU. Can't mix data due to GDPR etc. Train separate models, merge weights -> generalize to the combined dataset!
So there ya go: it's possible to mix trained models like mixing potions, no pre-training or fine-tuning necessary.

That said, there are still loads of open questions left! I'm v curious to see where LMC and model patching work goes in the future πŸš€
Also plenty of exciting possible applications to federated learning, distributed training, deep learning optimization, and so forth
Ok, that's enough for one thread... Check out algos, counterexamples, proofs, and more in

the paper (arxiv.org/abs/2209.04836)
and code (github.com/samuela/git-re…)
Joint work with Jonathan Hayase and @siddhss5. Inspired by work from @colinraffel, @rahiment, @jefrankle, @RAIVNLab folks, and many other beautiful people!

Shout out to @Mitchnw, @adityakusupati, @RamanujanVivek and others who came along the ride!
Oh I forgot to add: our weight matching algo (sec 3.2) runs in ~10 seconds. So you won't be waiting around all day!

β€’ β€’ β€’

Missing some Tweet in this thread? You can try to force a refresh
γ€€

Keep Current with Samuel Ainsworth is in NYC

Samuel Ainsworth is in NYC Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @SamuelAinsworth

Jan 9, 2023
Prediction: @Microsoft will launch an AI assistant product in the next 5 years, built on ChatGPT. It will blow Google Assistant, Amazon Alexa, etc out of the water 🌊

Think about it... 1/n
Imagine @AdeptAILabs's demo video () but with ChatGPT and working with any app running on Windows...

MSFT already has the necessary puzzle pieces in place:
1. Exclusive access to ChatGPT/related tech, thanks to their close partnership with @OpenAI
2. Enough cloud infra and capital to support running ML models for millions of users (@Azure)
3. A rich app and developer ecosystem that they control top to bottom (.NET, Windows Dev ecosys.)
4. Hardware chops (from Surface, etc) matching or exceeding G Nest, Alexa, and the rest
Read 7 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

:(