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variational-inference
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eigenfoo
commented
Dec 17, 2019
Kind of embarrassingly late to realize this, but PyMC3 doesn't properly distribute the Apache license as described in the license file. All we need to do is to add the frontmatter from the license with the current year at The PyMC Developers as the copyright holder.
I might have some time soon to file a PR for this, but if so
st--
commented
Dec 9, 2019
We should demonstrate in all notebooks how to (correctly) use tf.function. This would unearth bugs e.g. due to use of static instead of dynamic shapes (#1179) that aren't currently covered by any tests.
Seminars DeepBayes Summer School 2018
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Dec 17, 2019 - Jupyter Notebook
yell
opened
Jun 8, 2018
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
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Dec 14, 2019 - Python
Lecture notes on Bayesian deep learning
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Dec 19, 2019
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more
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Dec 20, 2019 - Jupyter Notebook
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
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Dec 19, 2019 - Jupyter Notebook
Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
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Dec 19, 2019 - Jupyter Notebook
I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
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Dec 19, 2019
Collection of probabilistic models and inference algorithms
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Dec 16, 2019 - Python
Implementation of VLAE
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Dec 19, 2019 - Python
Tensorflow implementation of conditional variational auto-encoder for MNIST
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Nov 25, 2019 - Python
jeff-regier
commented
Jan 4, 2017
The Gaia dataset is an extremely accurate catalog of bright stars' locations. We can use them to learn the WCS transforms for each image rather than relying on the SDSS pipeline.
It's already stored on the NERSC file system.
/global/project/projectdirs/cosmo/work/gaia/chunks-sdss
In addition to getting more accurate WCS transforms, we might also hope to correct for a property of
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
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Dec 17, 2019 - Python
Understanding normalizing flows
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Dec 16, 2019 - Jupyter Notebook
pabfer
commented
Jul 6, 2019
Describe the bug
If batch_size for MinibatchInferenceLoop is larger than the dataset size, this line throws due to division by zero.
Expected behavior
Possible mitigations are:
- Option No. 1: Default batch size to min("requested batch size", "dataset size") and issue a warning if "requested
Implementation of Sequential Attend, Infer, Repeat (SQAIR)
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Dec 10, 2019 - Jupyter Notebook
Kalman Variational Auto-Encoder
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Nov 24, 2019 - Python
avehtari
commented
Nov 26, 2015
From Lu Cheng:
"It was said in GPStuff manual page 42 that periodic kernel was coming
from this paper
http://jmlr.org/proceedings/papers/v33/solin14.pdf
In page 907, equation (23) and GPStuff appendix, there is the canonical
periodic covariance function. And it is not obvious to find the explicit
form of quasi-periodic covariance function in section 3.5.
In the demo_periodic.m, there is alway t
PyTorch implementation of "Weight Uncertainty in Neural Networks"
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Dec 18, 2019 - Jupyter Notebook
Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
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Dec 19, 2019 - MATLAB
Deep Generative Models for Natural Language Processing, conference mapping and paper list
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Dec 20, 2019
Implementation of Sequential Variational Autoencoder
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Dec 19, 2019 - Python
PixelVAE with or without regularization
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Dec 19, 2019 - Python
TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC"
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Oct 22, 2019 - Python
Code for the paper Implicit Weight Uncertainty in Neural Networks
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Dec 18, 2019 - Jupyter Notebook
Adversarial Imitation Via Variational Inverse Reinforcement Learning
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Dec 12, 2019 - Python
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Similar to the tutorial on custom losses in SVI, we should have a tutorial on implementing custom MCMC kernels using the new MCMC API. Something simple like SGLD seems like a good starting point.