
phd student in machine learning @oxcsml @UniofOxford 🐳 previously research intern @Apple, computational maths @Stanford, theoretical physics @imperialcollege
California, USA
Joined October 2017
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Emilien Dupont retweeted
Matrix multiplication is the basic computational operation that powers modern computing (including AI). Yet, the theoretical fastest speed at which computers can multiply matrices (omega ω) is still unknown and has been a longstanding challenge for complexity theory and computer science.
Today, we announce a new record for omega (ω<2.371177). This is the result of a great team effort between @GoogleDeepMind, our academic collaborators, and our Gemini-powered coding agent AlphaEvolve! 🧮
arxiv.org/abs/2608.16884v1
We introduce 🌸✨ AlphaEvolve ✨🌸, an evolutionary coding agent using LLMs coupled with automatic evaluators, to tackle open scientific problems 🧑🔬 and optimize critical pieces of compute infra ⚙️
deepmind.google/discover/blo…
Emilien Dupont retweeted
📢New Paper on Reward Modelling📢
Ever wondered how to choose the best comparisons when building a preference dataset for LLMs?
Our latest paper revives classic statistical methods to do it optimally!
Here’s a 🧵on how it works 👇
arxiv.org/abs/2502.04354
Emilien Dupont retweeted
Take a look at our latest work. One of my proudest works so far :)
Image-generation diffusion models can draw arbitrary visual-patterns. What if we finetune Stable Diffusion to 🖌️ draw joint actions 🦾 on RGB observations?
Introducing 𝗚𝗘𝗡𝗜𝗠𝗔
paper, videos, code, ckpts: genima-robot.github.io/
🧵Thread⬇️
Emilien Dupont retweeted
🚨Important update from our Robot Learning Lab in London.
Following recent news, we’re moving on after a wonderful 2 years…
Today, we unveil 4 big pieces of research from our incredible team. Check out the compilation video and thread below to see our final work! 📽️👇
We build neural codecs from a *single* image or video, achieving compression performance close to SOTA models trained on large datasets, while requiring ~100x fewer FLOPs for decoding ⚡ #CVPR2024
c3-neural-compression.github…
For technical details, please refer to the paper and code.
📜: arxiv.org/abs/2312.02753
🧑💻: github.com/google-deepmind/c…
⚙️: c3-neural-compression.github…
We hope this is a step towards making neural codecs a practical reality ✨
We present #FunSearch in @Nature today - a system combining LLMs with evolutionary search to generate new discoveries in math and computer science! 👩🔬🔬✨
Introducing FunSearch in @Nature: a method using large language models to search for new solutions in mathematics & computer science. 🔍
It pairs the creativity of an LLM with an automated evaluator to guard against hallucinations and incorrect ideas. 🧵 dpmd.ai/x-funsearch
Work done with the amazing @ber24, @BarekatainAmin, @SashaVNovikov, @matejbalog, @Mpawankumar123, @franciscuto, @JSEllenberg, @PengmingWang, Omar Fawzi, @pushmeet, @AlhusseinFawzi!
Emilien Dupont retweeted
We construct neural processes by iteratively transforming a simple stochastic process into an expressive one, similar to flow/diffusion-based models, but in function space!
Join us at our #NeurIPS2023 poster session: neurips.cc/virtual/2023/post… on Wednesday morning!
Emilien Dupont retweeted
Introducing Manifold Diffusion Fields (MDF), our new work on learning generative models over fields defined on curved geometries. This is joint work with our intern @Ahmed_AI035 (who hasn’t even started his PhD yet!) and @jsusskin at @Apple MLR arxiv.org/abs/2305.15586 🧵
Manifold Diffusion Fields
present Manifold Diffusion Fields (MDF), an approach to learn generative models of continuous functions defined over Riemannian manifolds. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF represents functions using an explicit parametrization formed by a set of multiple input-output pairs. Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold. Empirical results on several datasets and manifolds show that MDF can capture distributions of such functions with better diversity and fidelity than previous approaches
paper page: huggingface.co/papers/2305.1…
Emilien Dupont retweeted
We introduce a new class of stochastic process models, which are constructed by stacking sequences of neural parameterised Markov transition operators in function space.
🗞️ arxiv.org/abs/2305.15574
w/ @emidup @kasparmartens @tom_rainforth
@yeewhye
A thread
Emilien Dupont retweeted
Drop by our #ICLR2023 workshop tmrw (Thurs) on "Neural Fields Across Fields: Methods and Applications of INRs"!
Schedule: sites.google.com/view/neural…
Accepted Papers: sites.google.com/view/neural…
In-Person: Room MH4 (room used for poster sessions)
Virtual: iclr.cc/virtual/2023/worksho…
Emilien Dupont retweeted
Very happy to announce that our latest paper on Neural data compression with INRs, Meta Learning & Sparse Subnetwork selection has been accepted to #ICML2023 (Scores 7, 7, 7). 1/N
Paper: arxiv.org/abs/2301.09479
Emilien Dupont retweeted
Can deep transformers be trained without skip connections nor normalisation layers?
Our ICLR 2023 paper shows you how, using wide NN signal propagation ideas. We hope this can potentially pave the way to more efficient deep LLMs! (1/9)
Paper: arxiv.org/abs/2302.10322

















