How Neural Networks Handle Probabilities

Probabilistic generative models and neural networks that represent complex datasets—like images or environmental measurements—using hidden latent variables, and researchers who design these models to capture underlying structure.

Neural network designers who must represent probability distributions efficiently, and statistical models that rely on parametric families like Gaussians to approximate complex real-world data.

Guide or recognition networks (q_\phi(z\mid x)) that approximate posterior distributions over latents, and the generative models (p_\theta(x, z)) they help train through importance-weighted sampling.

Variational autoencoders, free energy principle models, and any probabilistic neural network trained via variational inference rely on the evidence lower bound (ELBO) as a core training objective.

Practitioners training variational autoencoders and related models who often optimize squared reconstruction error without always recognizing its probabilistic interpretation.