ELBO
The evidence lower bound (ELBO) is the optimization objective in variational inference, defined as the difference between the expected log-likelihood and the KL divergence, forming a lower bound on the marginal likelihood. Maximizing the ELBO simultaneously encourages data fit and drives the approximate posterior toward the prior.
Timeline (last 90 days)
LinSlot constructs a probabilistic graphical model connecting images, slots (objects), and blocks (attributes), and is trained by optimizing the Evidence Lower Bound (ELBO).
In Bayesian neural networks with continuously increasing width M, the KL divergence regularization term in the ELBO gradually overwhelms the expected log-likelihood term, causing the variational predictive distribution to degenerate into the prior predictive distribution.
Likelihood temperature scaling raises the likelihood function to the power of 1/T (T < 1), which is equivalent to multiplying the KL term in the ELBO by a coefficient T.
The derivation of DDPM relies on the variational lower bound (ELBO) and Markov chain assumptions, involving probabilistic tools such as KL divergence and conditional Gaussian distributions.
The gap between the ELBO and the true marginal likelihood equals the KL divergence between the approximate posterior and the true posterior, KL(q(z|x) || p(z|x)).
The ELBO loss function consists of two components: a reconstruction loss, which ensures generation quality, and a KL divergence, which constrains the regularity of the latent distribution.
The VAE training objective is to maximize the Evidence Lower Bound (ELBO), which is equivalent to simultaneously optimizing the reconstruction loss and the KL divergence terms.
VAEs can only optimize a lower bound on the likelihood (ELBO), while GANs do not directly model probability density at all.
All Facts (8)
LinSlot constructs a probabilistic graphical model connecting images, slots (objects), and blocks (attributes), and is trained by optimizing the Evidence Lower Bound (ELBO).
50%UnverifiedIn Bayesian neural networks with continuously increasing width M, the KL divergence regularization term in the ELBO gradually overwhelms the expected log-likelihood term, causing the variational predictive distribution to degenerate into the prior predictive distribution.
50%UnverifiedLikelihood temperature scaling raises the likelihood function to the power of 1/T (T < 1), which is equivalent to multiplying the KL term in the ELBO by a coefficient T.
50%UnverifiedThe derivation of DDPM relies on the variational lower bound (ELBO) and Markov chain assumptions, involving probabilistic tools such as KL divergence and conditional Gaussian distributions.
50%UnverifiedThe gap between the ELBO and the true marginal likelihood equals the KL divergence between the approximate posterior and the true posterior, KL(q(z|x) || p(z|x)).
50%UnverifiedThe ELBO loss function consists of two components: a reconstruction loss, which ensures generation quality, and a KL divergence, which constrains the regularity of the latent distribution.
50%UnverifiedThe VAE training objective is to maximize the Evidence Lower Bound (ELBO), which is equivalent to simultaneously optimizing the reconstruction loss and the KL divergence terms.
50%UnverifiedVAEs can only optimize a lower bound on the likelihood (ELBO), while GANs do not directly model probability density at all.
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