Danilo J. Rezende @DaniloJRezende
Director @ #DeepMind Building models to accelerate fundamental sciences Prev @EPFL IFT @Polytechnique @fisicaUSP danilorezende.com London, England Joined September 2012-
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New preprint! “Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation” arxiv.org/abs/2404.19739… FlowMol is a flow matching model that jointly samples the geometric and topological structure of molecules. We also present surprising results on flow…
Simple life on our planet originated some 3.8 billion years ago, and since then trillions of organisms across billions of species have lived and have died, the vast majority of them representing evolutionary dead ends. Currently, Earth is home to almost nine million different…
Also, let's not teach kids to reason like LLMs! I've met many teachers, even at the university level, simply test students by having them regurgitate "recipes". Students, in turn, often request more problem sets, hoping to see similar questions on exams. 😕
Also, let's not teach kids to reason like LLMs! I've met many teachers, even at the university level, simply test students by having them regurgitate "recipes". Students, in turn, often request more problem sets, hoping to see similar questions on exams. 😕
@cHHillee asked for an example of why you might want to ignore the constraint you're optimising over, and so i've knocked together the picture i had in my head
@cHHillee asked for an example of why you might want to ignore the constraint you're optimising over, and so i've knocked together the picture i had in my head https://t.co/4PiRfqCI0n
The "much of GR and EM" mentioned here are just the Bianchi identities, which would be true regardless of the equations of motion. The real content of Maxwell's equations is not dF=0 (which is automatic from F=dA), but rather d*F=*J. Similarly for GR.
The "much of GR and EM" mentioned here are just the Bianchi identities, which would be true regardless of the equations of motion. The real content of Maxwell's equations is not dF=0 (which is automatic from F=dA), but rather d*F=*J. Similarly for GR.
Exactly!
The reason the universe seems fine-tuned for us is that evolution fine-tuned us for the universe.
Our SIMA tech report is now on arXiv! arxiv.org/abs/2404.10179
Our SIMA tech report is now on arXiv! arxiv.org/abs/2404.10179
RNNs *are* state-space models. This seems more like linear vs non-linear SSM distinction.
This looks like interesting work, but … RNNs, Mamba, S4, etc. are *all* state-space models, at least according to the control theory terminology that has been in use since at least 1960!
This looks like interesting work, but … RNNs, Mamba, S4, etc. are *all* state-space models, at least according to the control theory terminology that has been in use since at least 1960!
I'm going to be a bit of a grinch, but who makes these decisions? Where are they announced? Where are they discussed? What is the rationale? Was there community input? Is there a long-term plan/vision (or will NeurIPS chairs 2025 drop it on a whim?)?
I'm going to be a bit of a grinch, but who makes these decisions? Where are they announced? Where are they discussed? What is the rationale? Was there community input? Is there a long-term plan/vision (or will NeurIPS chairs 2025 drop it on a whim?)?
“Understand really well the simple things” is the best description of how I think one should do Science!!!
I talked about our position paper on "Video as the New Language for Real-World Decision Making" arxiv.org/abs/2402.17139 on the TWIML podcast @twimlai. Check out the conversation below:
I talked about our position paper on "Video as the New Language for Real-World Decision Making" arxiv.org/abs/2402.17139 on the TWIML podcast @twimlai. Check out the conversation below:
💯 Scientific discovery is about at the tails of existing knowledge/data, machine learning is about the bulk of the existing data. Doesn't mean the latter cannot assist with the former, but it's highly non trivial.
💯 Scientific discovery is about at the tails of existing knowledge/data, machine learning is about the bulk of the existing data. Doesn't mean the latter cannot assist with the former, but it's highly non trivial.
@togelius Then, like now, the secret ingredient of effective prompt engineering was for the prompter to already know the answer they needed.
Introducing SIMA: the first generalist AI agent to follow natural-language instructions in a broad range of 3D virtual environments and video games. 🕹️ It can complete tasks similar to a human, and outperforms an agent trained in just one setting. 🧵 dpmd.ai/3TiYV7d
Implicit steerable kernels are taking off! 🚀 We significantly simplified the code and now support steerable convolutions on both regular and irregular grids! 🛠️Code: github.com/maxxxzdn/impli… 🗒️ Google Colab tutorial: colab.research.google.com/drive/1mE_9GRW… 🧵1/8
"For a successful technology, reality must take precedence over public relations, for Nature cannot be fooled." Richard P. Feynman
Soumith Chintala @soumithchintala
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28K Followers 687 Following Global Lead, IBM Quantum advocacy & education| PhD @PittTweet | @DickinsonCol | “Sufficiently amusing” | Ask me about my qubit.Antoine Bosselut @ABosselut
3K Followers 602 Following Helping machines make sense of the world. Asst Prof @ICepfl; Before: @stanfordnlp @allen_ai @uwnlp @MSFTResearch #NLProc #AIIt isn't meant to be a dunk on interpretability, I did some work here myself. Rather, my point is that there is very little value imo in getting a sin-cos mumbo-jumbo for the majority of cases.
I am Vienna for @iclr_conf #ICLR2024 for a week presenting work done with @loreloc_ @diegocalanzone & Christian Jimenez. Ping me if you want to chat about - realiable and complex probabilistic reasoning 🎯📊 - neuro-symbolic integration 🤖🧠 - fast inference⚡️🔬
Mixture models that subtract probability density can be exponentially more expressive in representing complex distributions We prove this when they are learned by squaring tensorized networks, and show how they represent a unifying modelling framework in our #ICLR2024 spotlight!
Honestly, of all the properties of KANs except "mathematical" (lol), interpretable is the least convincing to me. This animation is 100% interpretable, but how does it help? Plus, one can as well poke any NN post hoc and get a symbolic expression (see Cranmer et al.) if needed.
float i,e,R,s;vec3 q,p,d=(.5-FC.rgb/r.y)*rotate3D(7.,vec3(sin(t),0,1));for(q--;i++<1e2;i>50.?d/=-d:d){e+=i/1e4;o.rgb+=hsv(.1,p.y,e/80.);s=1.;p=q+=d*e*R*.3;p=vec3(log(R=length(p))-t,-p.z/R,atan(p.x,p.y));for(e=p.y-1.;s<6e3;s+=s)e+=cos(exp(dot(cos(p*s),cos(p.zxy*s))-1.))/s;}
It’s often much more impactful to do something well than to do it “first”.
It fails to capture extremely basic principles of visual reality, like object permanence. Which any two-year-old has mastered. But can we improve it? Sure. What you're going to do is increase the sampling density of the input:target space that you're fitting a curve to. You're…
With Sora, the evidence is piling up quickly. Now we're operating at scale. And the evidence will keep piling up with new models going forward -- even faster. And the evidence so far is resounding -- you really are looking at latent-space collages and interpolations. The inner…
Video generation models and Neural Radiance Fields have been improving regularly since 2016, and now they're in the spotlight. As a result there's a been a lot of debate about whether such systems embed a *model of physics*. Let's take a look...
a new kind of science! (building in public!) with the legendary @stephen_wolfram
cleaning up the house (lab) cause there’s a sudden weekend guest 😅 and it’s stephen wolfram 😱
The central premise of computing is that the cosmos is computable, giving us a means to build an enchanted land not only by controlling the world at a fidelity once relegated to the power of the gods and goddesses of our myths, but also by building new worlds into which we might…
poast lightsabers! (this is my old one, the new one… well, may the fourth be with me)
is CS just a really young field with poorly developed abstractions compared to math, physics, etc? what makes this different than a celebrated mathematician using only intro-level algebra and shunning algebraic topology?
Robots + AI are the next great frontier We demo’d some of the latest AI on our robot during 60 Minutes last week Figure-01 is connected to a pertained model via OpenAI to output common sense reasoning So when Bill says “hand me something healthy” the robot, by visual reasoning…
This is such a brilliant project. Thanks for sharing @d_yuqing
Animals are intelligent agents that plan and act to accomplish complex goals. Can we try learning from them? We present EgoPet, a new ego centric video dataset of animals scraped from YouTube and TikTok.
can not really prompt chatgpt 4 into having good physical intuition it needs more than words
🍩Topological Deep Learning (TDL) processes data on topological domains: simplicial complexes, hypergraphs, and more. it extends graph neural nets with notable performance gains 🌟 Interested? Join this ICML'24 GRaM coding challenge to collaborate on the latest TDL research!🏆
Very excited to announce the 2nd edition of the Topological Deep Learning Challenge, hosted at @icmlconf 2024 by @GRaM_workshop 🌐 Ready to go beyond the graph domain? Implement a topological lifting, and get published! 💫 Challenge website📢: pyt-team.github.io/packs/challeng…
The #AISTATS2024 Test of Time Award for the most impactful AISTATS 2014 paper goes to Rajesh Ranganath, Sean Gerrish, and David Blei for "Black Box Variational Inference". Many thanks to the awards committee, and congratulations!
This makes me happy! I admire the work and the people so much. It was a milestone moment and marked a paradigm shift in the relationship between AI/Ml and stats. I remember the 2016 tutorial at NeurIPS where I first met Rajesh. 🎉
The #AISTATS2024 Test of Time Award for the most impactful AISTATS 2014 paper goes to Rajesh Ranganath, Sean Gerrish, and David Blei for "Black Box Variational Inference". Many thanks to the awards committee, and congratulations!
This was a fun talk to give. The conference was great. Happy to see such a strong effort in Europe related to AI for fundamental physics.
And finally @KyleCranmer with a big picture summary