# Questions tagged [normalizing-flow]

Random variate representations based on repeated one-to-one transforms of a standard random variable which produce generative models and closed-form densities.

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### Can normalizing flows approximate bounded distributions in deep learning?

I’m exploring the use of normalizing flows in deep learning for generative modeling and I have a specific requirement: my target distributions are bounded (for example, between 0 and 1). I understand ...
119 views

### Proof for expressive power of flow-based models: Normalizing Flows for Probabilistic Modeling and Inference by Papamakarios et al

I've been reading the great summary work on Normalizing Flows "Normalizing Flows for Probabilistic Modeling and Inference" by Papamakarios et al.. A few questions regarding a proof came up ...
410 views

### Why aren't Normalizing Flows suitable for Discrete Distributions?

I am currently trying to understand why normalizing flows are not applicable to discrete distributions (a quick primer on NF can be found here). The assumptions on the transformation f between the ...
32 views

### How to train a continuous-time normalizing flow model?

I'm confused on how we can actually train a continuous-time normalizing flow model. There are two use cases for the discrete-time (original) normalizing flows, and I've tried to outline how I would do ...
53 views

### How can we use ReLU activation in a Normalizing Flow model? More generally, is differentiable almost everywhere enough for a normalizing flow?

In some works, e.g., enter link description here normalizing flow models are considered with ReLU activation. For example, using a planar flow, $f = f_n \circ ... \circ f_1$, and each $f_i$ has the ...
2k views

### Normalizing flows as a generalization of variational autoencoders?

Normalizing flows are often introduced as a way of restricting the rigid priors that are placed on the latent variables in Variational Autoencoders. For example, from the Pyro docs: In standard ...
1 vote
12 views

### Help finding Bayesian model with multi-modal posterior [closed]

Background: There is a paper (link) that concerns combining MCMC methods with a normalizing flow (a type of generative model). The basic idea is that the normalizing flow helps propose samples, which ...
305 views

### loss function that penalizes empirical CDF

I have been doing literature review of generative models. From what I gather, there are likelihood based generative models that model the likelihood and use it as objective function to learn the ...
1 vote
24 views

### At what circumstances will the difficulty for the tasks of density evaluation and sampling be different?

In this tutorial video of normalizing flow, the presenter mentioned that for the original autoregressive flow, the density evaluation is fast and the sampling is slow. In contrast, for the inverse ...
167 views

### What is the difference between copulas and normalizing flows?

The goal of normalizing flows is to produce arbitrarily complex probability-distributions from a simple distribution (usually the Normal distribution) through learning an invertible transform. Copulas ...
1 vote
53 views

### Choice of base distribution in normalizing flows for product distribution

I'm currently trying to implement a normalizing flow (NF) to efficiently sample from a product distribution $p=fg$. Contrary to most examples I've come across, I actually know how to sample ...
95 views

809 views

### Vector-Jacobian Product Computational Cost

The paper FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models presents a continuous-time flow as a generative model which uses Hutchinson's trace estimator to give an ...
2k views

### Normalizing flow training

I've been learning about normalizing flow. This is my understanding, and please correct me whenever I am wrong. There are $\{y_1,y_2,...,y_n\}$ samples from an unknown distribution $p_y(y)$ that we ...
91 views

1 vote