ML for Scientists: L8
Carol(ina) Cuesta-Lazaro
This week in AI4Science
p(\mathrm{World}|\mathrm{Prompt})
["Genie 2: A large-scale foundation model" Parker-Holder et al (2024)]
Probabilistic ML has made high dimensional inference tractable
1024x1024xTime
["Genie 3: A new frontier for world models" Parker-Holder et al (2025)]
GANS
Deep Belief Networks
2006
VAEs
Normalising Flows
BigGAN
Diffusion Models
2014
2017
2019
2022
A folk music band of anthropomorphic autumn leaves playing bluegrass instruments
Contrastive Learning
2023
2026
"Write a C compiler"
AGI?
Why should I care about uncertainties?
Decision making
Decision making in science
Is the current Standard Model ruled out by data?
Mass density
Vacuum Energy Density
0
CMB
Supernovae
\Omega_m
\Omega_\Lambda
Observation
Ground truth
Prediction
Uncertainty
Is it safe to drive there?
True
Reconstructed
\delta_\mathrm{Obs}
\delta_\mathrm{ICs}
p(\delta_\mathrm{ICs}, \theta|\delta_\mathrm{Obs})
["Joint cosmological parameter inference and initial condition reconstruction with Stochastic Interpolants" Cuesta-Lazaro, Bayer, Albergo et al NeurIPs 2024 ML for the Physical Sciences]
Bridging two distributions
x_1
x_0
Base
Data
How is the bridge constrained?
Normalizing flows: Reverse = Forward inverse
Diffusion: Forward = Gaussian noising
Flow Matching: Forward = Interpolant
is p(x0) restricted?
Diffusion: p(x0) is Gaussian
Normalising flows: p(x0) can be evaluated
Is bridge stochastic (SDE) or deterministic (ODE)?
Diffusion: Stochastic (SDE)
Normalising flows: Deterministic (ODE)
(Exact likelihood evaluation)
"Creating noise from data is easy;
creating data from noise is generative modeling."
Yang Song
Copy of ML For Scientists: L8
By carol cuesta
