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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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 ...
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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 ...
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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 ...
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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 ...
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Normalizing Flows notations and change of variable formula
Maybe it is a silly question, but I am wondering why in some papers the function $f$ is the mapping from $x$ to $z$ and in some others it is the other way around. Typically in this review we go from $...
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Can we ignore the generation side of the method described in density estimation using Real NVP?
First appologies if my question is stupid. I am studying the paper "Density estimation using real NVP" by Dinh, Sohl-Dickstein and Bengio. link
The paper presented a nice idea that the generation ...
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Normalizing Flow Penalization
I am looking to train a normalizing flow, specifically a Masked Autoregressive Flow model. However, this model leads to high variance on lower dimensional, less complex data. I am using a neural ...
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Planar flows implementation to approximate Gamma distribution
I've been trying to implement in order to approximate Gamma distribution but the problem I've been encountering is that the hyperbolic tangent activation function that I used gives negative values, ...
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Proof for expressive power of flow-based models: Normalizing Flows for Probabilistic Modeling and Inference by Papamakarios et al
I've ben reading the great summary work on Normalising Flows "Normalizing Flows for Probabilistic Modeling and Inference" by Papamakarios et al..
A few questions regarding a proof came up as ...
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Inverse Neural Networks
Suppose there is a series of transformation applied to the random variable $z_0$ such that
$$
z_M = f_{\theta_{M}} \circ f_{\theta_{M-1}} \circ \ldots \circ f_{\theta_{1}}(z_0) =: f_{\theta}(z_0).
$$
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