Prasanna1991/pytorch-vae

Variational Autoencoder in Pytorch

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anujinho/trident

Official repository for the paper TRIDENT: Transductive Decoupled Variational Inference for Few Shot Classification

Language: Python - Size: 195 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 15 - Forks: 1

ioangatop/machine_learning_2

Repository of UvA's lab exercises for the course "Machine Learning 2" 2019.

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shangqigao/BayeSeg

The official implementation of "Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation" via Pytorch

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huangyh09/brie-tutorials

Tutorials for using BRIE

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lubaroli/dust

This is the code repository for the DuSt-MPC paper, published at Robotics: Science and Systems (RSS) 2021.

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sbrml/gaussian_processes

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tillahoffmann/vbzero

Minimal variational Bayes in torch.

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Wei2624/AI_Learning_Hub

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

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vishwakftw/Pump-Failure-Prediction 📦

Coursework pertaining to CS5350 : Bayesian Data Analysis offered in Spring 2019

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sanjaythakur/Unifying-VI-and-PAC-Bayes-for-Learning-that-Scales 📦

Accompanying code for a paper

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nbip/notMIWAE

Code accompanying the notMIWAE paper

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kingofspace0wzz/wae-rnf-lm

Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" https://arxiv.org/abs/1904.02399

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addtt/ladder-vae-pytorch

Ladder Variational Autoencoders (LVAE) in PyTorch

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mark-antal-csizmadia/variational-inference-gmm

Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.

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bghojogh/Generative-LLE

The code for Generative Locally Linear Embedding (GLLE).

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ronaldseoh/bayesian-dl-experiments

Bayesian deep learning experiments

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sungyubkim/MCDO

A pytorch implementation of MCDO(Monte-Carlo Dropout methods)

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AaltoML/t-SVGP

Codes for 'Dual Parameterization of Sparse Variational Gaussian Processes' (NeurIPS 2021)

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Jianningli/skullVAE

training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

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soroosh-rz/Bayesian-Methods-for-Machine-Learning

Bayesian Methods for Machine Learning

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AaltoML/boundary-gp

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

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KodAgge/AdvancedMachineLearning

A collection of the assignments in the course advanced machine learning

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kzhai/PyLDA

A Latent Dirichlet Allocation implementation in Python.

Language: Python - Size: 72.9 MB - Last synced at: over 2 years ago - Pushed at: about 6 years ago - Stars: 52 - Forks: 22

nec-research/microbiome-mvib

Microbiome-based disease prediction with multimodal variational information bottlenecks, Grazioli et al., PLOS Computational Biology 2022

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singhalrk/stein_ksd

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jundsp/vblds

Variational Inference of Bayesian Linear Dynamical Systems. EM algorithm to infer and learn the dynamics of time-series data.

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Siris2314/AutoVariate

A python package made to streamline the usage of Variational Autoencoders, understand the algorithm first before using this package

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manuwhs/DTU-Course-Projects

A set of Course projects developed during the MSc on Mathematical Modelling and Computation at DTU

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ronaldseoh/ronald_bdl

An experimental Python package for learning Bayesian Neural Network.

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PGM-Lab/probai-2021-pyro

Repo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)

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gianlucadetommaso/Stein-variational-samplers

Python and MATLAB code for Stein Variational sampling methods

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yesiltepe-hidir/vae-unified

Collection of VAE paper implementations: Conditional VAE (CVAE), VQ-VAE, Wasserstein Autencoders (WAE), Inverse Autoregressive Flow VAE

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declanoller/haskell-vae

Learning about Haskell with Variational Autoencoders

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HareeshBahuleyan/tf-var-attention

Tensorflow Implementation of Variational Attention for Sequence to Sequence Models (COLING 2018)

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tvlearn/tvo

A PyTorch package for Truncated Variational Optimization.

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SourangshuGhosh/Doubly-Stochastic-Deep-Gaussian-Process

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.

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xxl4tomxu98/anomamly-detection-autoencoder

Anomaly Detections and Network Intrusion Detection, and Complexity Scoring.

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vrvrv/Variational-Inference-with-Normalizing-Flows

Unofficial Pytorch Lightning implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015]

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ldv1/bbvi_spike_and_slab

Black-box spike and slab variational inference, example with linear models

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gallego-posada/machine_learning

Machine Learning 2016/2017 - MSc Artificial Intelligence @ UvA

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WilliamYi96/Variational-Inference

Generative Models

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nbip/IWAE

Importance Weighted Autoencoders in TensorFlow 2, reproducing results from the IWAE paper with 1 or 2 stochastic layers

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maremita/evoVGM

Evolutionary-based Variational Generative Model using Multiple Sequence Alignments

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joocxi/tf2-VAEAC

Variational Autoencoder with Arbitrary Conditioning for Image Inpainting in TensorFlow 2.0

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DannieLu/BV-NICE

A generative Bayesian estimation framework for individualized treatment effect (ITE) estimation, integrates representation learning, adversarial matching and causal estimation.

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gulfaraz/balancing-shape-texture-bias-in-imagenet-trained-vgg19

A study on the effect of normalization in predictions by CNN models

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AliLotfi92/Deep_Variational_Information_Bottleneck

Tensorflow implementation of deep variational information bottleneck

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Droliven/DHMP_jittor

Jittor reimplementation of DiverseSampling (MM22)

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oskarfernlund/orthogonal-SVGP

Orthogonal sparse variational Gaussian processes (SVGP's) in TensorFlow.

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lliutianc/mirt-iwvae

Language: Python - Size: 17.6 KB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

brain-iith/SOD_DGP

Subset of Data Variational Inference for Deep Gaussian Process Model

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jpfitzinger/PENN-for-py

Tutorial on the construction of explainable parameter encoder neural networks using tensorflow in python.

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christiando/SGPD_Inference

Libary for SGPD (Sigmoidal Gaussian Process Density) inference

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buzzer4mornin/CTMP-ThesisProject

Master Thesis @ Charles University - "Probabilistic Models for Recommender Systems"

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andersy005/probabilistic-programming-and-bayesian-with-PyMC3

Implementation of probabilistic programming and Bayesian algorithms in Python, based on C. Davidson's Bayesian Methods for Hackers

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DanielAnthes/VAE

TensorFlow implementation of a simple Variational Autoencoder. Includes a simple GUI for exploring the learned latent space.

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SourangshuGhosh/Stochastic_LatentDirchletAnalysis

Python implementation of Stochastic Variational Inference for LDA

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wataruhashimoto52/nmf_tfp

Implementation of Bayesian Non-negative Matrix Factorization using Variational Inference with TensorFlow Probability

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amoretti86/PSVO

Implementation of Particle Smoothing Variational Objectives

Language: Python - Size: 102 MB - Last synced at: almost 2 years ago - Pushed at: about 5 years ago - Stars: 6 - Forks: 1

MohamedHmini/Paysim-Fraud-Detection-using-VAE-and-BNN

fraud detection on the paysim dataset, with the use of VAE to perform data-augmentation and BNN to classify the datapoints.

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JasperRice/DD2434

Machine Learning, Advanced Course

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RonyAbecidan/VAE

Here, I am studying the pionneering paper of VAEs called "Auto-Encoding Variational Bayes" written by Diederik P. Kingma and Max Welling.

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ldv1/Variational_Pitman-Yor_Process_Infinite_Mixture_of_GPs

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zhenkewu/lotR

🌲 Integrating Sample Similarity Information into Latent Class Models: A Tree-Structured Shrinkage Approach

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zhenkewu/doubletree

🎄🎄 Nested Latent Class Models for Domain-Adaptive and Semi-supervised Learning of Cause-of-Deaths using Verbal Autopsy

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weichengv/HEBAE

Hierarchical Empirical Bayes Auto-Encoder

Language: Python - Size: 4.42 MB - Last synced at: over 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 1

mark-antal-csizmadia/DD2434-VAE-Project

Replication of the research paper titled Auto-Encoding Variational Bayes.

Language: Python - Size: 8.35 MB - Last synced at: over 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

frankaging/generative-physics-inference

Slip or Not? Unsupervised Learning to Understand Physical Scene Using Multimodal Variational Physics Inference Network

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chris-tng/pizza

VAE-variant generative models, powered by PyTorch

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zhiming-xu/transformer-vae-paraphrase

Transformer-based conditional variational autoencoder for paraphrase generation.

Language: Python - Size: 6.76 MB - Last synced at: over 2 years ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 1

zafarali/generative

Repository to explore Generative Models

Language: Python - Size: 9.77 KB - Last synced at: 2 months ago - Pushed at: over 8 years ago - Stars: 7 - Forks: 2

DongjunLee/vae-tensorflow

TensorFlow implementation of Auto-Encoding Variational Bayes.

Language: Python - Size: 679 KB - Last synced at: 2 months ago - Pushed at: over 7 years ago - Stars: 8 - Forks: 2

senya-ashukha/sparse-vd-pytorch

Sparse Variational Dropout a a Minimal Working Example

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georgedeath/how-bayesian-should-BO-be

How Bayesian should Bayesian Optimisation be?

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gtancev/Bayesian-Machine-Learning

Markov chain Monte Carlo and variational inference.

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caravagnalab/VIBER

VIBER - Variational Binomial Mixtures

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rachtsingh/lgamma

Implementations of polygamma, lgamma, and beta functions for PyTorch

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tallamjr/bap-numpyro

Bayesian Analysis in Python (2nd ed.) with Numpyro

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cambridge-mlg/arch_uncert

Code for "Variational Depth Search in ResNets" (https://arxiv.org/abs/2002.02797)

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IAmS4n/Matrix-Factorization

Finding of the missed values in the adjacency matrix of a big undirected weighted graph by utilizing probabilistic graphical models. The adjacency matrix's values were modeled with Poisson distribution and Gamma prior.

Language: Python - Size: 3.35 MB - Last synced at: over 2 years ago - Pushed at: almost 5 years ago - Stars: 2 - Forks: 0

enceladus-rex/normalizing-flows

An implementation of variational normalizing flows using TF2

Language: Python - Size: 326 KB - Last synced at: 6 months ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 1

lpcinelli/probabilistic-nn

Probabilistic approach to neural nets - modern scalable approximate inference methods

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bellthomas/VDQN

Exploring Variational Deep Q Networks. A study undertaken for the University of Cambridge's R244 Computer Science Masters Course. Inspired by https://arxiv.org/abs/1711.11225/.

Language: Python - Size: 12.7 MB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 2 - Forks: 1

tr7200/Vadam

Keras implementation of arXiv 1806.04854

Language: Python - Size: 49.8 KB - Last synced at: over 2 years ago - Pushed at: about 4 years ago - Stars: 4 - Forks: 3

lewisKit/Amortized_SVGD

Experiments of amortized stein variational gradient

Language: Terra - Size: 2.02 MB - Last synced at: over 2 years ago - Pushed at: about 8 years ago - Stars: 14 - Forks: 8

haziqj/phd-thesis

My PhD thesis: Regression modelling using priors depending on Fisher information covariance kernels (I-priors)

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mark-antal-csizmadia/variational-inference

Factorized variational approximation using a univariate Gaussian distribution over a single variable x.

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ebonilla/AutoGP

Code for AutoGP

Language: Python - Size: 93.8 KB - Last synced at: over 1 year ago - Pushed at: almost 6 years ago - Stars: 26 - Forks: 13

wmkouw/LARX

ForneyLab.jl factor node for a latent autoregressive model with exogenous input.

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christiando/SGCP_Inference

Language: Python - Size: 27.3 KB - Last synced at: about 2 years ago - Pushed at: about 6 years ago - Stars: 1 - Forks: 2

andrehuang/normalizing-flows-reproduce

Reproduction and extension of "Variational inference with normalizing flows"

Language: Jupyter Notebook - Size: 18.9 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 8 - Forks: 1

yxnchen/BATF

MATLAB code for 「Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model」.

Language: MATLAB - Size: 20.2 MB - Last synced at: over 2 years ago - Pushed at: over 4 years ago - Stars: 8 - Forks: 1

iivek/dag-constrained-nmf

Nonnegative matrix factorization with DAG constraints. A probabilistic formulation, variational learning.

Language: Matlab - Size: 829 KB - Last synced at: about 2 years ago - Pushed at: over 7 years ago - Stars: 4 - Forks: 0

lijingyiran/vae-uncertainty-metrics

Variational Auto-Encoder with uncertainty quantification using BNN and variational inference.

Language: Julia - Size: 252 KB - Last synced at: over 2 years ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

lavish619/Variational-Autoencoders

Variational Autoencoders implementation in Keras.

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idnavid/misc

Language: Jupyter Notebook - Size: 1.91 MB - Last synced at: over 2 years ago - Pushed at: almost 6 years ago - Stars: 7 - Forks: 1

amoretti86/AETF

Autoencoding Topographic Factors

Language: Python - Size: 3.67 MB - Last synced at: over 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 0

imgemp/untapped

Semi^2-Supervised Deep Generative Models in Theano

Language: Python - Size: 109 KB - Last synced at: 7 months ago - Pushed at: about 6 years ago - Stars: 1 - Forks: 0

MauroCE/Dissertation

Third year mathematics dissertation on variational, laplace and mcmc approximations of bayesian logistic regression

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