Neural structure learning with stochastic differential equations.
Benjie WangJoel JenningsWenbo GongPublished in: ICLR (2024)
Keyphrases
- structure learning
- stochastic differential equations
- bayesian networks
- graphical models
- maximum a posteriori estimation
- brownian motion
- conditional independence
- parameter estimation
- fractional brownian motion
- transfer learning
- network structure
- image processing
- sample size
- additive gaussian noise
- least squares
- stochastic process
- active learning
- differential equations
- non stationary
- markov networks
- dynamic bayesian networks
- heavy traffic
- approximate inference
- long range
- belief propagation
- data points