#
variational-inference
Here are 233 public repositories matching this topic...
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
python
theano
statistical-analysis
probabilistic-programming
bayesian-inference
mcmc
variational-inference
-
Updated
Nov 7, 2020 - Python
st--
commented
Oct 22, 2020
Feature request
Our Scipy optimizer wrapper should explicitly handle unconnected gradients.
Motivation
Currently, the GPflow Scipy optimizer wrapper (gpflow.optimizers.Scipy) simply errors (with one of the typically unhelpful TensorFlow error messages) when one of the passed-in variables is not connected to the loss to be minimized. This can easily happen in a valid case, for exa
Seminars DeepBayes Summer School 2018
-
Updated
Aug 24, 2019 - Jupyter Notebook
danielkelshaw
commented
Apr 30, 2020
Hi @JavierAntoran @stratisMarkou,
First of all, thanks for making all of this code available - it's been great to look through!
Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
learning
machine-learning
deep-neural-networks
deep-learning
tensorflow
deep
pytorch
vae
unsupervised-learning
variational-inference
probabilistic-graphical-models
variational-autoencoder
autoregressive-neural-networks
-
Updated
Mar 30, 2020 - Python
Boltzmann Machines in TensorFlow with examples
machine-learning
deep-learning
tensorflow
keras
restricted-boltzmann-machine
rbm
dbm
boltzmann-machines
mcmc
variational-inference
gibbs-sampling
ais
sklearn-compatible
tensorflow-models
pcd
contrastive-divergence-algorithm
energy-based-model
annealed-importance-sampling
-
Updated
Aug 1, 2020 - Jupyter Notebook
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
python
pytorch
bayesian-network
image-recognition
convolutional-neural-networks
bayesian-inference
bayes
bayesian-networks
variational-inference
bayesian-statistics
bayesian-neural-networks
variational-bayes
bayesian-deep-learning
pytorch-cnn
bayesian-convnets
bayes-by-backprop
aleatoric-uncertainties
-
Updated
Sep 27, 2020 - Python
Lecture notes on Bayesian deep learning
-
Updated
Feb 11, 2018
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
variational-inference
semantic-segmentation
nips
generative-models
variational-autoencoders
u-net
nips-2018
neurips
neurips-2018
-
Updated
Sep 25, 2020 - Jupyter Notebook
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
deep-learning
generative-model
vae
awesome-list
representation-learning
unsupervised-learning
variational-inference
variational-autoencoder
variational-bayes
disentanglement
variational-auto-encoder
disentangled-representations
-
Updated
Oct 4, 2020
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..
tutorial
generative-adversarial-network
dcgan
generative-model
gaussian-mixture-models
auto-regressive-model
gans
bayesian-classifiers
variational-inference
tutorial-code
variational-autoencoder
cyclegan
-
Updated
Jan 21, 2019 - Jupyter Notebook
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
point-cloud
registration
gaussian-mixture-models
expectation-maximization-algorithm
variational-inference
3d
dual-quaternion
point-cloud-registration
open3d
coherent-point-drift
non-rigid-registration
rigid-transformations
filterreg
dual-quaternion-skinning
-
Updated
Nov 8, 2020 - Python
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
machine-learning
natural-language-processing
computer-vision
deep-learning
neural-network
semi-supervised-learning
convolutional-networks
neural-machine-translation
representation-learning
gaussian-processes
variational-inference
kernel-learning
variational-autoencoders
deep-kernel-learning
-
Updated
Jun 5, 2019
Collection of probabilistic models and inference algorithms
python
machine-learning
bayesian
bayesian-inference
mcmc
variational-inference
gibbs-sampling
dirichlet-process
probabilistic-models
-
Updated
Apr 3, 2020 - Python
Implementation of VLAE
feature-extraction
generative-model
representation-learning
unsupervised-learning
variational-inference
-
Updated
Dec 21, 2017 - Python
Statistical Rethinking (2nd ed.) with NumPyro
python
numpy
variational-inference
causal-inference
bayesian-statistics
markov-chain-monte-carlo
laplace-approximation
-
Updated
Oct 29, 2020 - Jupyter Notebook
DGMs for NLP paper list
natural-language-processing
parsing
text-generation
generative-text
generative-model
graphical-models
mcmc
generative-adversarial-networks
variational-inference
markov-chain-monte-carlo
latent-variable-models
normalizing-flows
structured-prediction
generative-models
variational-autoencoders
approximate-inference
-
Updated
Sep 25, 2020
Tensorflow implementation of conditional variational auto-encoder for MNIST
tensorflow
mnist
autoencoder
vae
variational-inference
conditional
denoising-autoencoders
cvae
denoising-images
denoising
variational-autoencoder
conditional-vae
-
Updated
Apr 25, 2017 - Python
Statistical Rethinking (2nd Ed) with Tensorflow Probability
tensorflow
tutorials
bayesian-inference
variational-inference
bayesian-statistics
markov-chain-monte-carlo
tensorflow-probability
statistical-rethinking
-
Updated
Aug 26, 2020 - Jupyter Notebook
Scalable inference for a generative model of astronomical images
astronomy
graphical-models
bayesian-inference
astronomical-catalogs
variational-inference
astronomical-images
-
Updated
Apr 26, 2020 - Jupyter Notebook
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
-
Updated
Sep 3, 2018 - Python
Understanding normalizing flows
-
Updated
Dec 2, 2019 - Jupyter Notebook
PyTorch implementation of "Weight Uncertainty in Neural Networks"
-
Updated
Mar 26, 2019 - Jupyter Notebook
Kalman Variational Auto-Encoder
deep-learning
neural-network
graphical-models
variational-inference
kalman-filter
variational-autoencoder
-
Updated
Feb 12, 2019 - Python
Bayesian coherent point drift for Windows 10
matching
kd-tree
point-cloud
outlier-detection
bayesian-inference
unsupervised-learning
variational-inference
bayesian-statistics
shape-analysis
nystrom
approximate-inference
point-cloud-registration
shape-matching
coherent-point-drift
rigid-registration
non-rigid-registration
point-set-registration
shape-deformation
-
Updated
Mar 22, 2020
Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
-
Updated
Nov 4, 2020 - MATLAB
GPstuff - Gaussian process models for Bayesian analysis
regression
octave
classification
survival-analysis
bayesian
spatial-analysis
bayesian-inference
expectation-propagation
mcmc
gaussian-processes
variational-inference
bayesian-optimization
covariance-functions
-
Updated
Aug 5, 2020 - MATLAB
meissnereric
commented
Oct 22, 2018
This may be useful for Inference algorithms to use during automatic gradient chaining.
Implementation of Sequential Attend, Infer, Repeat (SQAIR)
tracking
detection
motion
vae
generative
variational-inference
approximate-inference
representations-learning
sqair
vimco
iwae
-
Updated
Apr 9, 2019 - Jupyter Notebook
Improve this page
Add a description, image, and links to the variational-inference topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the variational-inference topic, visit your repo's landing page and select "manage topics."
Currently our non-notebook examples are manually included in the examples webpage via custom
.rstfiles intutorial/source/. As the number of examples increases, it would be better to follow NumPyro's approach and generate HTML pages automatically withsphinx_gallery. This would make examples easier t