We approach this question by focusing on individual neurons, analyzing the behavior of their activations at different timescales.

We approach this question by focusing on individual neurons, analyzing the behavior of their activations at different timescales.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. It's important because the Fisher matrix is known to be much better for nonlinear optimization than the Hessian. The experiment that you mention show that |H| is also much better than the Hessian. So the big question is, which is better: |H| or F, where both matrices are used in the same setup. However, this assumption is clearly false due to the presence of the large number of saddle points in high dimensional spaces.

Accordingly, we can directly induce the final instance-level classifier upon the discriminator.

We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data. The explanation is causal in the sense that changing learned latent factors produces a change in the classifier output statistics. To construct these explanations, we design a learning framework that leverages a generative model and information-theoretic measures of causal influence.

We assume we have access to a set of i.i.d.

Adam is a widely used optimization method for training deep learning models.