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