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Thomas Kipf

@tkipf

Research at Google DeepMind. Ex-Physicist. Controllable World Simulators (GNNs, Structured World Models, Neural Assets). TLM Veo Capabilities (Ingredients & more). πŸ“ San Francisco, CA

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24.10.2024
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Latest posts by Thomas Kipf @tkipf

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NeSy conference The NeSy conference studies the integration of deep learning and symbolic AI, combining neural network-based statistical machine learning with knowledge representation and reasoning from symbolic appr...

Recordings of the NeSy 2025 keynotes are now available! πŸŽ₯

Check out insightful talks from @guyvdb.bsky.social, @tkipf.bsky.social and D McGuinness on our new Youtube channel www.youtube.com/@NeSyconfere...

Topics include using symbolic reasoning for LLM, and object-centric representations!

29.11.2025 08:21 πŸ‘ 7 πŸ” 3 πŸ’¬ 0 πŸ“Œ 0

Yes don’t try this at home

27.05.2025 22:44 πŸ‘ 6 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

Working on Veo's ingredients to video feature has been a blast. Check it out on flow.google

27.05.2025 20:05 πŸ‘ 2 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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Two life updates:

1) About a year ago I decided to join the Veo team to work on capabilities. It’s been a fun ride! Excited for what’s still to come.

2) I've been busy caring for a newborn the past couple of days πŸ₯° Excited for the incredible world he will grow up in. Veo's impression below:

27.05.2025 20:04 πŸ‘ 32 πŸ” 0 πŸ’¬ 5 πŸ“Œ 0

Check out @tkipf.bsky.social's post on MooG, the latest in our line of research on self-supervised neural scene representations learned from raw pixels:

SRT: srt-paper.github.io
OSRT: osrt-paper.github.io
RUST: rust-paper.github.io
DyST: dyst-paper.github.io
MooG: moog-paper.github.io

13.01.2025 15:25 πŸ‘ 13 πŸ” 3 πŸ’¬ 0 πŸ“Œ 0

I'm excited to announce that I have no idea what day of the week it is and I'm hoping I can keep this up for the rest of the year

01.01.2025 19:50 πŸ‘ 103 πŸ” 4 πŸ’¬ 3 πŸ“Œ 0

Congrats!! Lots to think about

20.12.2024 02:53 πŸ‘ 2 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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I gave a talk on Compositional World Models at NeurIPS last week 🌐

The recording is now online: neurips.cc/virtual/2024... (for registered attendees; starts at 6:06:00)

Workshop: compositional-learning.github.io

19.12.2024 01:57 πŸ‘ 40 πŸ” 4 πŸ’¬ 1 πŸ“Œ 0

Welcome to Google!

02.12.2024 21:34 πŸ‘ 1 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

That’s a great recommendation, thanks!

30.11.2024 02:16 πŸ‘ 1 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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Yet our first two days looked like this πŸ˜„

29.11.2024 22:15 πŸ‘ 2 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0

Thanks, Durk!

29.11.2024 18:04 πŸ‘ 0 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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Blue skies over Joshua Tree 🌌

29.11.2024 16:53 πŸ‘ 61 πŸ” 0 πŸ’¬ 5 πŸ“Œ 0

Sending reminders really shouldn’t be something we have to deal with manually. Clearly there’s headroom in designing better incentive structures.

25.11.2024 17:50 πŸ‘ 4 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0

I think there is still *a lot* of headroom for automation while ultimately reducing potential for human error (or just laziness on the AC part).

25.11.2024 17:50 πŸ‘ 1 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0

I think that depends on the conference. ICLR pretty much already automated the reviewer assignment part using a new bidding system that seemed to work pretty well. Manual AC assignments were heavily discouraged, only minor adjustments were needed.

25.11.2024 17:49 πŸ‘ 0 πŸ” 0 πŸ’¬ 2 πŸ“Œ 0

Yeah, it'll have to be a tightly kept secret among people who enter the exclusive AC circle πŸ™ƒ

22.11.2024 21:35 πŸ‘ 3 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

Hot take: 90% of what ACs/SACs do could in principle already be automated (with the remaining 10% being process oversight and borderline decision making).

At least right now, it seems like reviewers have the more important job for the most part.

22.11.2024 17:22 πŸ‘ 27 πŸ” 0 πŸ’¬ 7 πŸ“Œ 0
Login β€’ Instagram Welcome back to Instagram. Sign in to check out what your friends, family & interests have been capturing & sharing around the world.

Veo + DreamScreen! www.instagram.com/p/DCpFZ_UMyN...

21.11.2024 19:11 πŸ‘ 7 πŸ” 1 πŸ’¬ 0 πŸ“Œ 0

Agreed, important to find the right balance. Deeply caring about something doesn’t mean one should neglect other aspects of life (especially health, sleep, nutrition, social connection, downtime, …).

21.11.2024 19:05 πŸ‘ 1 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

Waymo deserves to be the number one tourist attraction in San Francisco right now, and it's not even close

For like ~$11 you get to ride in a genuine self-driving car with up to four people!

Wildly entertaining

21.11.2024 15:51 πŸ‘ 130 πŸ” 5 πŸ’¬ 10 πŸ“Œ 3

Being totally obsessed with your work really helps with motivation and with getting things done. Exciting times.

21.11.2024 05:53 πŸ‘ 65 πŸ” 2 πŸ’¬ 3 πŸ“Œ 0

πŸ™‹β€β™‚οΈ

20.11.2024 00:02 πŸ‘ 4 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0

Let’s welcome @ellis.eu to Bluesky and give them a follow! πŸ¦‹

19.11.2024 16:11 πŸ‘ 36 πŸ” 7 πŸ’¬ 0 πŸ“Œ 1

Hello World!

16.11.2024 19:52 πŸ‘ 128 πŸ” 33 πŸ’¬ 4 πŸ“Œ 6
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Thrilled to announce Boltz-1, the first open-source and commercially available model to achieve AlphaFold3-level accuracy on biomolecular structure prediction! An exciting collaboration with Jeremy, Saro, and an amazing team at MIT and Genesis Therapeutics. A thread!

17.11.2024 16:20 πŸ‘ 609 πŸ” 204 πŸ’¬ 18 πŸ“Œ 25

We're planning to open source, but no ETA yet. Stay tuned :)

15.11.2024 21:55 πŸ‘ 4 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0

We'll present this work at NeurIPS (Spotlight, yay πŸ™Œ) this year - come find us at the poster soon or reach out if you have questions!

This was a fun project with an amazing set of collaborators (and co-leads Sjoerd van Steenkiste and @zdanielz.bsky.social) at Google DeepMind / Google Research.

15.11.2024 06:09 πŸ‘ 7 πŸ” 0 πŸ’¬ 0 πŸ“Œ 0
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MooG can provide a strong foundation for different scene-centric downstream vision tasks, including point tracking, monocular depth estimation, and object tracking.

Especially when reading out from frozen representations, MooG is competitive with on-the-grid baselines.

15.11.2024 06:09 πŸ‘ 1 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0
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Under the hood, MooG uses two independent cross-attention mechanisms to write to – and read from – a *set* of latent tokens that are consistent over time.

Think of it as a scene memory consisting of a set of tokens that can flexibly bind to individual scene elements.

15.11.2024 06:09 πŸ‘ 2 πŸ” 0 πŸ’¬ 1 πŸ“Œ 0