Introducing AlphaGenome: an AI model to help scientists better understand our DNA – the instruction manual for life 🧬 Researchers can now quickly predict what impact genetic changes could have - helping to generate new hypotheses and drive biological discoveries. ↓

An opportunity to join our team. Feel free to reach out if you have any questions.

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I’m really excited to be attending NeurIPS and presenting our work on efficient fine-tuning of pre-trained diffusion models for SOTA conditional generation. Come chat with us on 12th Dec (Thursday) at 11am! Thread below (🧵) -

A highlight of the #NeurIPS2023 meetup in Cambridge for me was Carl Rasmussen's keynote on "Halting Climate Change". Spoiler: it has nothing to do with AI. Worth a watch:

Links: code: https://t.co/Isu9mByt2N slides: https://t.co/Tf9VQqSZlv paper:

Introducing a novel Langevin-based conditional sampler, our model scales to fit complex scalar/vector fields in both Euclidean & manifold codomains, exemplified by forecasting tropical hurricanes as per the illustrated figure.

We achieve this by first extending diffusion models to infinite-dimensional function spaces by a) constructing a noising process which admits, as limiting distribution, a Gaussian process that is group invariant, and b) approximating the score with an equivariant neural network.

As shown in the figure, these feature fields transform in a particular way when acted on by group elements such as translation or rotations. Often the frame used to represent these fields is arbitrary, and as such one ought to encode this invariance into the model

Our work presents a general-purpose probabilistic model over feature fields, which are functions often encountered in physical sciences, such as temperature maps, wind fields, stress tensors, etc.

Happy to share Geometric Neural Diffusion Processes at #NeurIPS. Join us for Poster Session 3 on Wednesday if you’re interested in diffusion models for modelling equivariant stochastic processes. With @MathieuEmile @MHutchinson141 @ValentinDeBort1 @yeewhye #richturner

as well as a keynote presentation by prof Carl Rasmussen on his latest research on climate change policy. Finally, prof @lawrennd will chair a panel discussion on "AI for Social Good".

Apart from posters, the day will feature two spotlight talk sessions...

There are still a couple of places left for the NeurIPS@Cambridge meetup next week, Dec 8th. Priority will be given to people presenting a poster. Registration & full schedule on

Happy to announce the 3rd NeurIPS @ Cambridge event on Friday, Dec 8th! Join us for an in-person gathering to discuss the latest in machine learning research. Find all details and register at https://t.co/9QnJFqttRo. #NeurIPS2023 #MachineLearning #Cambridge

Als u vandaag één artikel leest, laat het dan dit zijn. "Alex was 41. In de kranten staat dat hij een heroïneverslaafde was. Dat zal zeker zijn, maar hij was ook een oorlogsslachtoffer, een mens en bovenal mijn vriend." https://t.co/gaL4yKZEts

Our #ICML2023 paper introduces Neural Diffusion Processes - generalising diffusion processes to function spaces. Great to work with @vdutor @alandanielsaul and @ZoubinGhahrama1 Paper: https://t.co/gXhIQ2xRFF Code: https://t.co/6zHgZb8VN7

in case you ever wondered what happens when a sphere gets hit with the ReLU activation🌐🧠🔥

Lol. I also have to thank T-Rex for my first job. Learned to play the game (from raw pixels in a screen capture) using deep Q-learning: https://t.co/J2JnlxlDiI

Congrats! It's amazing to see how Random (Fourier) Features continue to fuel progress even in this era of Transformers

This an excellent opportunity to present your work (past, NeurIPS or ongoing) and to connect with other researchers in our diverse community here in the greater Cambridge area :) The day will also feature panel sessions and in-person presentations.