How does score matching utilize the Fisher Information Matrix to learn the parameters of a probabilistic model without normalizing the score?

What score matching does: learns the gradient of the log-density without normalizing

Score matching approximates log-density gradients for variational inference without normalization

What denoising score matching does: learns to denoise, which equals learning the score

Denoising score matching learns to remove noise, enhancing signal representation and interpretation

What importance sampling does: reweights samples from proposal to estimate target expectation

Importance sampling reweights samples from a proposal distribution to approximate the expectation of a target distribution

What weight tying does in language models: shares embedding and output projection matrices

Language models use tied weights to share embedding and output projection matrices, enhancing parameter efficiency

What maximum likelihood estimation does: find θ maximizing P(data|θ)

Maximizes θ to maximize the probability of observed data given θ

What IS (Inception Score) measures: diversity and quality of generated images

Inception Score quantifies image diversity and generated images' quality