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Hello, this is a great job
There is a problem, in the experiment Conditioner=DAG, Normalizer=Monotonic
How to calculate the inverse transformation of Graphical Normalizing Flow?
That is, how to get…
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Hey again @alexhernandezgarcia , @josephdviviano and others,
**TL;DR**: Can I use Stack(Grid,ContinuousCube) as an environment to sample N*3D-points ($N \times ℝ^3$ vectors)?
**Long Version**
I…
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Thank you for sharing this awesome work!
I was trying to reproduce the normalizing flow used in the joint prior parts, and I noticed that the network structure (e.g. the PReLU implementation) might b…
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As discussed in #21 it would be nice to reproduce the results from sec. 6.1 in the "Variational inference using normalizing flows" paper by Rezende et al.
I would guess the approach is:
- sample inpu…
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Dear authors,
Thank you for making the code available. I am interested in semi-supervised normalizing flows and trying to reproduce the results. I couldn't find the code to generate new samples fr…
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I found the backflow of real NVP cannot reconstruct the original input data. This is caused by a bug in code when the dimension is more than 2:
https://github.com/karpathy/pytorch-normalizing-flow…
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# Title
NODEA - Neural Ordinary Differential Equations Anonymous
# Description
This sessions is supposed to be an introduction to neural ordinary differentiable equations (Neural ODEs). As on…
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Thanks for providing time-series compatible normalizing flows in [flows](https://github.com/zalandoresearch/pytorch-ts/blob/62a18cfd0f19fc2247fc7e79e77f44b8dab6c4c9/pts/modules/flows.py) file. I am in…
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Currently the code runs on one device, which doesn't allow scaling to larger computational network such as TPU pods.
Parallelizing over local sampler should be relatively simple, since that does no…
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I propose that we add a new FEP (free energy perturbation) module, as `dc.fep`.
Introduction
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Free energy perturbation has become an increasingly powerful technique in modern drug di…