This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the values of these features, and then training a model for each bin. They are then examining the differences between the models. Usually this is done to learn ...
Here is a paper with the mathematical definition of each term:
Let Nt,n,σ,L be all target functions that can be implemented using a
neural network of depth t, size n, activation function σ, and when we
restrict the input weights of each neuron to be |w|1 + |b| ≤ L.
To put it simply GANs suffer from a problem of uneven learning rate. Imagine the learning rate of a pitcher and hitter if the pitcher gets to a point where they can throw much better than the hitter can hit then the hitter may fall into a 'training pit' as to be unable to ever learn how to hit from the pitcher.
This follows a continues relationship in ...
If you already have two years of a bachelor's of mathematics, I recommend part I of Goodfellow et al.. Features:
This book is very recent.
This book is free.
This book reviews exactly the mathematics used in the optimization of neural nets (in part 1), and then actually goes through the various models in detail in the later parts. The review is done at a ...
Your mistake is that you think that the referenced $V(D,G)$ is the deifinition of the cross entropy! Indeed, the cross entropy is defined base on the negative value of the $V(D,G)$. Hence, if you consider the minus behind the $V(D,G)$ ($-V(D,G)$) the sentence will be meaningful.