# Tag Info

Accepted

### What loss function to use when labels are probabilities?

Actually, the cross-entropy loss function would be appropriate here, since it measures the "distance" between a distribution $q$ and the "true" distribution $p$. You are right, ...

### How can a probability density value be used for the likelihood calculation?

The probability density is used to 'measure how good' the parameters are because it is a natural way of quantifying if these parameters are good for the observed data. Also, as the notation often ...
Accepted

### Why is the Jensen-Shannon divergence preferred over the KL divergence in measuring the performance of a generative network?

Lets start with question 1) how does JS-divergence handles zeros? by definition: \begin{align} D_{JS}(p||q) &= \frac{1}{2}[D_{KL}(p||\frac{p+q}{2}) + D_{KL}(q||\frac{p+q}{2})] \\ &= \frac{...
Accepted

### Are the authors of the VAE paper writing the PDFs as a function of the random variables?

When it comes to notation/terminology, often, people in machine learning are (a bit?) sloppy, which causes a lot of confusion, especially for newcomers to the field or people not very math-savvy. I ...

### What is the difference between model and data distributions?

Yes. In Machine Learning we consider that the samples in your training set are sampled from an underlying distribution called the data generating distribution. Generative models classify the samples ...
Accepted

### How does $\mathbb{E}$ suddenly change to $\mathbb{E}_{\pi'}$ in this equation?

Also, in general, in the conditional expectation, which distribution do we compute the expectation with respect to? From what I have seen, in $\mathbb{E}[X|Y]$, we always calculate the expected value ...

### Why is KL divergence used so often in Machine Learning?

In ML we always deal with unknown probability distributions from which the data comes. The most common way to calculate the distance between real and model distribution is $KL$ divergence. Why ...

### Can the normalization factor for the belief state update be zero?

I think that the normalisation factor is assumed to be non-zero. So, in practice, I guess, you must eventually check that $P(z \mid b, a)$ is non-zero (even though, I guess, it will likely never be ...
Accepted

### In deep learning, do we learn a continuous distribution based on the training dataset?

Well, there are some questions here... Does it (Deep Learning) try to learn a continuous distribution based on the training-set and its corresponding mappings, and map unseen examples from this ...
Accepted

### How does maximum approximation of the posterior choose a distribution?

Introduction: MAP finds a point estimate! As opposed to your apparently current belief, in maximum a posteriori (MAP) estimation, you are looking for a point estimate (a number or vector) rather than ...
Accepted

### What is a probability distribution in machine learning?

Random variables You do not necessarily need to understand the concept of a random variable (r.v.) to understand the concept of a probability distribution, but the concept of a random variable is ...
Accepted

### Why do we regularize the variational autoencoder with a normal distribution?

If you are mathematically inclined, here is an article that discusses the reasoning. What I get as a take away is that the VAE forces the learned latent space to be Gaussian due to the KL divergence ...

### How can we "draw i.i.d" from any probability distribution?

As far as I know, it doesn't make sense to say that a probability distribution is i.i.d., as you're saying. The property i.i.d. is a property of a sequence of random variables. In your case, the ...
Accepted

### PPO: policy loss becomes nan

You might want to try substituting the exponentiation with a piecewise-defined function that uses a numerical approximation that is more numerically stable for low values of the exponent, such as ...
Accepted

### What does "the expectation is taken across different possible inputs, drawn from the distribution of inputs we expect the system to encounter" mean?

The language used here is confusing me, because it is discussing a "distribution", as in a "probability distribution", but then refers to inputs, which are data gathered from outside of any ...
Accepted

### Solving the supervised learning problem of learning $p(y \vert \mathbf{x})$ by using traditional unsupervised technologies to learn $p(\mathbf{x}, y)$

This is the definition of conditional probability + Total probability decomposition formula: $p(y|x) = \frac{p(y,x}{p(x)} = \frac{p(x,y)}{\sum_{y'}p(x,y')}$. The idea is to use some unsupervised ...
Accepted

### Many of the best probabilistic models represent probability distributions only implicitly

The probabilistic models that represent distributions implicitly are, for example, the GANs. (Goodfellow is one of the authors of the original GAN model). In the paper Variational Inference using ...