Zakhar Shumaylov
@ZakobianPhD student at @Cambridge_Uni; Previously ML Research Intern @Apple
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New article: "The geometry of data: the missing metric tensor and the Stein score" (blog.christianperone.com/2024/11/the-ge…). I show how you can derive a (efficient to compute) data manifold metric tensor with the Stein score alone ! Deep connections to diffusion, score-based models and physics.
We are organizing a local NeurIPS in Cambridge this year on the 6th of Dec. If you are around, come listen and share your work. All relevant topics/departments welcome!
📣 Registration to the NeurIPS meetup@Cambridge on Fri 6 Dec is now open! If you would like to discuss the ML research being presented at #Neurips2024 register here: buff.ly/3CpXQWw. Further details of the event can be found on the website: buff.ly/3YZFzbj
Our new paper in @NatMachIntell tells a story about how, and why, ML methods for solving PDEs do not work as well as advertised. We find that two reproducibility issues are widespread. As a result, we conclude that ML-for-PDE solving has reached overly optimistic conclusions.
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