Best explanation!

2:00 Summary 8:00 EBMs training, maximum likelihood training requires estimation of partition function, contrastive divergence requires samples to be generated (MCMC Langevin with 1000 steps), minimize Fischer divergence (score matching) instead of KL divergence 19:15 EBMs parameterize a conservative vector field of gradients of an underlying scalar function, score based models generalize this to an arbitrary vector field. EBMS directly model the log-likelihood, score based models directly model the score (no functional parameters). 29:15? Backprops in EBM, f_theta derivative is s_theta, Jacobian of s_theta 34:00 Fischer divergence between model and perturbed data 36:25 Noisy data q to remove trace of Jacobian in calculations 39:30 Linearity of gradient 42:15 Estimating the score of the noise perturbed data density is equivalent to estimating the score of the transition kernel (Gaussian noise density) 44:10 Trace of Jacobian removed from estimation, loss function is a denoising objective 46:05 Sigma of noise distribution as small as possible 48:55? Stein unbiased risk estimator trick, evaluating quality of an estimator without knowing ground truth, denoising objective 51:55 Denoising score matching, these two objectives are equivalent up to a constant, minimizing the bottom objective (denoising) is equivalent to minimizing the top objective (which estimates the score of the distribution convolved with Gaussian noise) 52:20? Individual conditionals 53:25 Reduced generative modelling to denoising 55:35? Tweedie's formula, alternative derivation of denoising objective 58:25 Interpretation of equations, conditional on x is correlated to the joint density of x and perturbed x, q sigma is the integral of the joint density, Tweedie's formula expresses x in terms of the perturbed x with an optimal adjustment (gradient of q sigma which is correlated to the density of x conditional on the perturbed x) 1:01:35? Jacobian vector products, directional derivatives, efficient to estimate using backprop 1:04:20 Sliced score matching single backprop (directional derivative), without slicing needs d backprops 1:07:00 Sliced score matching not on perturbed data 1:12:00 Langevin MCMC, sampling with score 1:14:35 Real world data tends to lie on a low dimensional manifold 1:21:00 Langevin mixing too slowly, mixture weight disappears when taking gradients