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📢 Physics + GPs + inverse problems using #ProbabilisticNumerics 📢
At #ICML2022 we show that probabilistic ODE solvers are not just fast, but also useful for solving inverse problems! Joint work with Filip Tronarp and @PhilippHennig5. More below 🧵
At #ICML2022 we show that probabilistic ODE solvers are not just fast, but also useful for solving inverse problems! Joint work with Filip Tronarp and @PhilippHennig5. More below 🧵
The gist is: When doing inference with traditional ODE solvers we ignore their numerical error. But by being "probabilistic about the numerics", we can fit _both the ODE and the data jointly_! Which e.g. allows us to better learn parameters of oscillatory systems:
Paper: proceedings.mlr.press/v162/tronarp22a
Experiments: github.com/nathanaelbosch…
Code in #julialang: github.com/nathanaelbosch…
And if you got curious about probabilistic ODE solvers, there's more this ICML:
Experiments: github.com/nathanaelbosch…
Code in #julialang: github.com/nathanaelbosch…
And if you got curious about probabilistic ODE solvers, there's more this ICML:
It’s out! Oh boy!
Probabilistic Numerics: big ideas for the internals of learning machines — and now also a BOOK, w/ @maosbot and @HansKersting!
What is #ProbabilisticNumerics, and why does it matter for ML? Thread below, with a link to a free pdf of the book at the end :)
Probabilistic Numerics: big ideas for the internals of learning machines — and now also a BOOK, w/ @maosbot and @HansKersting!
What is #ProbabilisticNumerics, and why does it matter for ML? Thread below, with a link to a free pdf of the book at the end :)
We hear a lot about *models* in ML. But what actually happens within the “machine” during learning is a *numerical* task: Optimization (for loss minimization), Integration (for Bayesian inference), Simulation (for control, RL, physics,..). Linear Algebra for, well, everything.
Numerical analysis is well established, so ML’ers tend to think of numerical methods as primordial, immutable. But a numerical routine is itself a (low-level) learning machine! Because it _estimates_ an _unknown_ quantity from _data_, for data that is _computed_, not collected.
