phd student in machine learning @oxcsml @UniofOxford 🐳 previously research intern @Apple, computational maths @Stanford, theoretical physics @imperialcollege

California, USA
Joined October 2017
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
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📢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
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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⬇️
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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! 📽️👇
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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!
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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…
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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
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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
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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
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Emilien Dupont retweeted
Previously we had introduced *functa*, a framework for representing data as neural functions (aka neural fields, INRs) and doing deep learning on them. In our recent work *spatial functa* we show how to scale up the approach to ImageNet-1k 256x256. 📝arxiv.org/abs/2302.03130
Ever wondered why deep learning is always done on array data?🤔 Happy to announce our work: From data to functa: Your data point is a function and you can treat it like one 📝arxiv.org/abs/2201.12204 w/ @emidup @arkitus @DaniloJRezende @danrsm, to appear in ICML22
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Emilien Dupont retweeted
Good news: the deadline for paper submissions to the #ICLR2023 workshop on Neural Fields has been extended to Friday 11th Feb AOE🎉 Note the workshop is non-archival, so we encourage recent submissions/publications to other venues. Please use this chance to advertize your work!
We're organizing an #ICLR2023 workshop titled: 🌟Neural Fields across Fields: Methods and Applications of Implicit Neural Representations🌟 sites.google.com/view/neural… Please submit your cool research (due: 3rd Feb)! Looking forward to seeing you on 05/05/23 @ Kigali, Rwanda 🇷🇼
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Two more weeks to submit to our ICLR'23 Workshop on Neural Fields: sites.google.com/view/neural… featuring amazing speakers. Co-organised with: @hyunjik11 @emidup @vincesitzmann @thunguyenphuoc @drsrinathsridha
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Emilien Dupont retweeted
We're organizing an #ICLR2023 workshop titled: 🌟Neural Fields across Fields: Methods and Applications of Implicit Neural Representations🌟 sites.google.com/view/neural… Please submit your cool research (due: 3rd Feb)! Looking forward to seeing you on 05/05/23 @ Kigali, Rwanda 🇷🇼
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Emilien Dupont retweeted
Functa is now open-sourced!💻 Hope it's useful to those of us exploring INRs/Neural fields 😀 github.com/deepmind/functa
Ever wondered why deep learning is always done on array data?🤔 Happy to announce our work: From data to functa: Your data point is a function and you can treat it like one 📝arxiv.org/abs/2201.12204 w/ @emidup @arkitus @DaniloJRezende @danrsm, to appear in ICML22
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Really excited to share our latest paper on sparse Meta-Learning. This is particularly useful to design modality-agnostic compression methods for Implicit Neural Representations (INRs). 1/4 arxiv.org/abs/2205.08957 w/ @yeewhye
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Emilien Dupont retweeted
Most data is processed by algorithms, but compressors (eg JPEG) are for human eyes. 🤓Our fix: formalize lossy compression that ensures perfect downstream predictions 🔥1000x gains vs JPEG on ImageNet🔥 arxiv.org/abs/2106.10800 w. Ben Bloem-Reddy @karen_ullrich @cjmaddison 1/9
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Emilien Dupont retweeted
Paper update: LieTransformer, our group equivariant attention based model, accepted to #ICML2021! Joint work with @MHutchinson141, @ShehZaidi, @charlinelelan, @emidup, @yeewhye Paper 📃: arxiv.org/abs/2012.10885 Code 🖥: github.com/oxcsml/lie-transf… Slides 🖼: bit.ly/3guj5HT
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Emilien Dupont retweeted
We propose group equivariant subsampling/upsampling layers. One can construct exact equivariant models by simply using them with (G-)Conv layers! 🗞️ arxiv.org/abs/2106.05886 💻 git.io/JZrdA w/ @hyunjik11 @tom_rainforth @yeewhye A thread 👇
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Interested in Kernel Methods, Causal Inference and Uncertainty quantification? In our newest work we introduce BayesIMP: Uncertainty Quantification for Causal Data Fusion ! arxiv.org/abs/2106.03477 Big thanks to @Chau9991 @javiergonzh @yeewhye @sejDino 1/n
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