Thomas Kipf

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Thomas Kipf

Thomas Kipf

@tkipf

Sr. Staff RS at @GoogleDeepMind. Veo Team. Priors: GNNs, Structured World Models, Neural Assets, Veo Ingredients Co-Lead, Veo Robotics

San Francisco, CA Katılım Haziran 2009
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Thomas Kipf
Thomas Kipf@tkipf·
My PhD thesis "Deep Learning with Graph-Structured Representations" is now available for download: hdl.handle.net/11245.1/1b63b9… -- It covers a range of emerging topics in Deep Learning: from graph neural nets (and graph convolutions) to structure discovery (objects, relations, events)
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Richard Higgins
Richard Higgins@relh_net·
@tkipf rip I should have posted mine all that way ago instead the repo just turned into my own paper
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Thomas Kipf
Thomas Kipf@tkipf·
There's finally a community implementation of Neural Assets (in PyTorch)! Go check it out 👇 Neural Assets was one of the first (and maybe even the first scalable?) solution(s) to the long-standing problem of multi-entity consistency in visual generative models. One of the most fun projects I had the chance to work on (with the amazing @Dazitu_616).
Mingtian@MingtianZhang

Just released an unofficial PyTorch reimplementation of Neural Assets arxiv.org/pdf/2406.09292. Check it out if you want to build on top of this github.com/Wenlin-Chen/ne…

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Thomas Kipf
Thomas Kipf@tkipf·
This is one of the most impressive world model projects I have seen. Very elegant and highly effective combination of an image retrieval mechanism (using 3D locations/views) and otherwise just pure generative modeling. This is the way.
Junyoung Seo@jyseo_cv

What if a world model could render not an imagined place, but the actual city? We introduce Seoul World Model, the first world simulation model grounded in a real-world metropolis. TL;DR: We made a world model RAG over millions of street-views. proj: seoul-world-model.github.io

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Thomas Kipf
Thomas Kipf@tkipf·
Very cool work on multi-player world models 🗺️🧑‍🤝‍🧑
Nataniel Ruiz@natanielruizg

Excited to show some surprising inventions on generative multiplayer games we made at Google with Stanford. We call the work MultiGen. I've always been inspired by early studios like id Software with Doom or Blizzard with Warcraft bringing networked video games to the next level. We are at the point in history where we can make strides like them, but for generative games. It's a strange feeling to be in the age of generative video games while still discovering how exactly to train the models and design the tools that make them useful. All of the tools that have been invented for classic game engines need to be redesigned for generative games. For example level and world design is not entirely possible with existing technology. We introduce editable memory to diffusion game engines that allow for design of new levels via a minimap. But we can easily imagine how this can be expanded with different creation tools. The end goal of this research direction is to allow game designers to be able to guide the generation process of their world, at the granularity that they prefer. Editable memory also allows us to add multiplayer to Generative Doom. We were amazed when we saw GameNGen some years ago, and now you can play it live with friends in real-time, on your couch or even online. Shared representations like our editable memory seem like the future for this type of experience. Models are, in some cases, expensive and approximate encoders but great interpolators and extrapolators. Leveraging their strengths lets you have completely new experiences that can be realized now and not in the distant future. This work was started at my previous team and continued in collaboration with Stanford. Congratulations to all for the discoveries.

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Weihua Hu
Weihua Hu@weihua916·
Excited to share that I’ve joined @Anthropic on the Knowledge team. Looking forward to working on model capabilities for knowledge work — things like deep research and multi-hop web search. Grateful for everything I learned at Perplexity and excited for what’s ahead.
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Drew Jaegle
Drew Jaegle@drew_jaegle·
I've joined the founding team of Project Prometheus as a Member of Technical Staff. The time to reshape how we understand and build in the physical world is now, and I'm excited to be part of this team and mission. Oh, and I'm moving to SF in April. Reach out if you're around!
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Andrew Lampinen
Andrew Lampinen@AndrewLampinen·
After 5.5 years (or 7 or 9, counting internships), today was my last day at Google/DeepMind. When I was in London recently, I walked through the two floors that were (most of) DeepMind when I first joined, and thought about how much the company and field have changed since then.
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Thomas Kipf
Thomas Kipf@tkipf·
Genuine feedback: I honestly can't tell whether you're writing this to highlight Europe's strength in AI or the opposite. If it's the former, I'd certainly include BFL in the list and point out that quite a few of the teams behind the models listed in the original tweet have significant presence of core researchers in Europe (but ofc as part of a US-based company). Llama 1 team was primarily Paris (x.com/ylecun/status/…), later ones not so much anymore I believe. (I also need to look up TabPFN and Tirex, never heard of those).
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Nick Matarese
Nick Matarese@nmatares·
HUGE update for @FlowbyGoogle - 2 new view modes (grid/batch) - Collections (folders) for asset management - @ naming in prompt box - Drag and drop with marquee selection - @ tagging in the prompt box - Dedicated preview/edit surfaces with history - Improved search with filters - Performance improvement EVERYWHERE. and SO MUCH MORE. Proud of this one and the team behind it.
Flow by Google@FlowbyGoogle

Today we're expanding Flow into a full AI creative studio. We’ve redesigned the experience and powerful new tools so you can draft, visualize, and refine your stories in a single, unbroken workflow. What’s new: ✨ An updated interface for seamless workflows 🖼️ An easier way to create with images and videos, together 🗣 Seamless prompting and editing with natural language We can't wait to see what you create!

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David Luan
David Luan@jluan·
I’ll be leaving Amazon at the end of this week to cook up something new! Thanks to the Adept deal, I’ve spent the last ~2 years learning from @ajassy et al while leading Amazon’s agents R&D effort and our San Francisco AI lab. As a childhood EC2 Micro Instance fanboy, it was fun to speedrun launching our own tier-1 AWS service. We scaled up the Adept agent recipes, did new RL research, and shipped it to AWS customers like Hertz, 1Password, and Amazon.com itself. And it's cool to see Nova Act on top of realevals.xyz (at least for now). There’s incredible work to be done at Amazon and I'm grateful for the opportunities to take on more here. But with AGI so close, I want to spend 100% of my time on teaching AI systems brand new capabilities. At OpenAI, I was lucky to incubate the first GPTs; at Adept, we went all-in on agents before anyone else–our tech/people now drive computer-use efforts at every major lab. I have a bet for what's next. ;) This wasn't an easy decision, and I'm sad to leave this wonderful team. I’m grateful for the trust our execs placed in me during an important moment for Amazon and the field. I'm excited to swing at the next idea!
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Thomas Kipf
Thomas Kipf@tkipf·
Interstellar soundtrack while cruising down I-280 at 65mph in a Waymo is the ultimate flow state
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Thomas Kipf
Thomas Kipf@tkipf·
Best place to orchestrate an armada of agents
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