206 Repositories
Python bayesian-layers Libraries
Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).
What is judgyprophet? judgyprophet is a Bayesian forecasting algorithm based on Prophet, that enables forecasting while using information known by the
Second Order Optimization and Curvature Estimation with K-FAC in JAX.
KFAC-JAX - Second Order Optimization with Approximate Curvature in JAX Installation | Quickstart | Documentation | Examples | Citing KFAC-JAX KFAC-JAX
Python code to control laboratory hardware and perform Bayesian reaction optimization on the MIT Make-It system for chemical synthesis
Description This repository contains code accompanying the following paper on the Make-It robotic flow chemistry platform developed by the Jensen Rese
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da
Visualizing Yolov5's layers using GradCam
YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di
A library to inspect itermediate layers of PyTorch models.
A library to inspect itermediate layers of PyTorch models. Why? It's often the case that we want to inspect intermediate layers of a model without mod
A variational Bayesian method for similarity learning in non-rigid image registration (CVPR 2022)
A variational Bayesian method for similarity learning in non-rigid image registration We provide the source code and the trained models used in the re
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022
Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le
Lightweight mmm - Lightweight (Bayesian) Media Mix Model
Lightweight (Bayesian) Media Mix Model This is not an official Google product. L
Hierarchical-Bayesian-Defense - Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference (Openreview)
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical V
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the
Learning Features with Parameter-Free Layers, ICLR 2022
Learning Features with Parameter-Free Layers (ICLR 2022) Dongyoon Han, YoungJoon Yoo, Beomyoung Kim, Byeongho Heo | Paper NAVER AI Lab, NAVER CLOVA Up
Learning Features with Parameter-Free Layers (ICLR 2022)
Learning Features with Parameter-Free Layers (ICLR 2022) Dongyoon Han, YoungJoon Yoo, Beomyoung Kim, Byeongho Heo | Paper NAVER AI Lab, NAVER CLOVA Up
Python package for concise, transparent, and accurate predictive modeling
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. 📚 docs • 📖 demo notebooks Modern
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"
Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces" This repo contains the implementation of GEBO algorithm.
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
Posterior temperature optimized Bayesian models for inverse problems in medical imaging Max-Heinrich Laves*, Malte Tölle*, Alexander Schlaefer, Sandy
Official Implementation of "Transformers Can Do Bayesian Inference"
Official Code for the Paper "Transformers Can Do Bayesian Inference" We train Transformers to do Bayesian Prediction on novel datasets for a large var
A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations
Overview Code and supplemental materials for Karduni et al., 2020 IEEE Vis. "A Bayesian cognition approach for belief updating of correlation judgemen
The unified machine learning framework, enabling framework-agnostic functions, layers and libraries.
The unified machine learning framework, enabling framework-agnostic functions, layers and libraries. Contents Overview In a Nutshell Where Next? Overv
Python implementation of Weng-Lin Bayesian ranking, a better, license-free alternative to TrueSkill
Python implementation of Weng-Lin Bayesian ranking, a better, license-free alternative to TrueSkill This is a port of the amazing openskill.js package
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How
Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce
Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing
This repository contains the scripts needed to generate the results from the paper Unity Propagation in Bayesian Networks Handling Inconsistency via U
Automatic generation of crypto-arts based on image layers
NFT Generator Автоматическая генерация крипто-артов на основе слоев изображения. Установка pip3 install -r requirements.txt rm -rf result/* Как это ра
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Natural Posterior Network This repository provides the official implementation o
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference, with the design goal of unifying the automation (of Monte Carlo simulations), user-friendliness (of the library), accessibility (from multiple programming environments), high-performance (at runtime), and scalability (across many parallel processors).
Bayesian Inference Tools in Python
BayesPy Bayesian Inference Tools in Python Our goal is, given the discrete outcomes of events, estimate the distribution of categories. Using gradient
Efficient Online Bayesian Inference for Neural Bandits
Efficient Online Bayesian Inference for Neural Bandits By Gerardo Durán-Martín, Aleyna Kara, and Kevin Murphy AISTATS 2022.
Simple mathematical operations on image, point and surface layers.
napari-math This package provides a GUI interfrace for simple mathematical operations on image, point and surface layers. addition subtraction multipl
Bayesian A/B testing
bayesian_testing is a small package for a quick evaluation of A/B (or A/B/C/...) tests using Bayesian approach.
Laplace Redux -- Effortless Bayesian Deep Learning
Laplace Redux - Effortless Bayesian Deep Learning This repository contains the code to run the experiments for the paper Laplace Redux - Effortless Ba
Fully Adaptive Bayesian Algorithm for Data Analysis (FABADA) is a new approach of noise reduction methods. In this repository is shown the package developed for this new method based on \citepaper.
Fully Adaptive Bayesian Algorithm for Data Analysis FABADA FABADA is a novel non-parametric noise reduction technique which arise from the point of vi
Code for "Unsupervised Source Separation via Bayesian inference in the latent domain"
LQVAE-separation Code for "Unsupervised Source Separation via Bayesian inference in the latent domain" Paper Samples GT Compressed Separated Drums GT
Bayesian Modeling and Computation in Python
Bayesian Modeling and Computation in Python Open access and Code This repository contains the open access version of the text and the code examples in
A python tutorial on bayesian modeling techniques (PyMC3)
Bayesian Modelling in Python Welcome to "Bayesian Modelling in Python" - a tutorial for those interested in learning how to apply bayesian modelling t
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.
Think Bayes 2 by Allen B. Downey The HTML version of this book is here. Think Bayes is an introduction to Bayesian statistics using computational meth
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Bayesian Neural Networks Pytorch implementations for the following approximate inference methods: Bayes by Backprop Bayes by Backprop + Local Reparame
Modeling cumulative cases of Covid-19 in the US during the Covid 19 Delta wave using Bayesian methods.
Introduction The goal of this analysis is to find a model that fits the observed cumulative cases of COVID-19 in the US, starting in Mid-July 2021 and
PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation
deep-hist PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation PyT
Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control
Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control.
Doing bayesian data analysis - Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke
Doing_bayesian_data_analysis This repository contains the Python version of the R programs described in the great book Doing bayesian data analysis (f
Prml - Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
Pattern Recognition and Machine Learning (PRML) This project contains Jupyter notebooks of many the algorithms presented in Christopher Bishop's Patte
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models
Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor
Statistical-Rethinking-with-Python-and-PyMC3 - Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath
Statistical Rethinking with Python and PyMC3 This repository has been deprecated in favour of this one, please check that repository for updates, for
Statistical Rethinking: A Bayesian Course Using CmdStanPy and Plotnine
Statistical Rethinking: A Bayesian Course Using CmdStanPy and Plotnine Intro This repo contains the python/stan version of the Statistical Rethinking
HAR-stacked-residual-bidir-LSTMs - Deep stacked residual bidirectional LSTMs for HAR
HAR-stacked-residual-bidir-LSTM The project is based on this repository which is presented as a tutorial. It consists of Human Activity Recognition (H
Using Bayesian, KNN, Logistic Regression to classify spam and non-spam.
Make Sure the dataset file "spamData.mat" is in the folder spam\src Environment: Python --version = 3.7 Third Party: numpy, matplotlib, math, scipy
Lamblayer: a minimal deployment tool for AWS Lambda layers
lamblayer lamblayer is a minimal deployment tool for AWS Lambda layers. lamblayer does, Create a Layers of built pip-installable python packages. Crea
Code for the paper: Adversarial Machine Learning: Bayesian Perspectives
Code for the paper: Adversarial Machine Learning: Bayesian Perspectives This repository contains code for reproducing the experiments in the ** Advers
MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice
Sampyl May 29, 2018: version 0.3 Sampyl is a package for sampling from probability distributions using MCMC methods. Similar to PyMC3 using theano to
Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo"
dblmahmc Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo" Requirements: https://github.com
Recursive Bayesian Networks
Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi
Library to enable Bayesian active learning in your research or labeling work.
Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components
Final term project for Bayesian Machine Learning Lecture (XAI-623)
Mixquality_AL Final Term Project For Bayesian Machine Learning Lecture (XAI-623) Youtube Link The presentation is given in YoutubeLink Problem Formula
An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.
An implementation of the methods presented in Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.
Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021
This folder contains the code for 'Scalable Variational Approaches for Bayesian Causal Discovery'. Installation To install, use conda with conda env c
Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems
Bayesian Deep Learning for Manufacturing 2.0 (dlmfg) Object Shape Error Response (OSER) Digital Lifecycle Management - In Process Quality Improvement
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
Graph Posterior Network This is the official code repository to the paper Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classifica
Python implementation of Bayesian optimization over permutation spaces.
Bayesian Optimization over Permutation Spaces This repository contains the source code and the resources related to the paper "Bayesian Optimization o
Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
PyTorch implementation of BERT and PALs Introduction Work by Asa Cooper Stickland and Iain Murray, University of Edinburgh. Code for BERT and PALs; mo
[NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)
Adversarial Attacks on Graph Classification via Bayesian Optimisation @ NeurIPS 2021 This repository contains the official implementation of GRABNEL,
This repository is for the preprint "A generative nonparametric Bayesian model for whole genomes"
BEAR Overview This repository contains code associated with the preprint A generative nonparametric Bayesian model for whole genomes (2021), which pro
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models
LMPBT Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"
Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).
Permuton-induced Chinese Restaurant Process Note: Currently only the Matlab version is available, but a Python version will be available soon! This is
Equivariant layers for RC-complement symmetry in DNA sequence data
Equi-RC Equivariant layers for RC-complement symmetry in DNA sequence data This is a repository that implements the layers as described in "Reverse-Co
Experiments with Fourier layers on simulation data.
Factorized Fourier Neural Operators This repository contains the code to reproduce the results in our NeurIPS 2021 ML4PS workshop paper, Factorized Fo
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model
Large scale and asynchronous Hyperparameter Optimization at your fingertip.
Syne Tune This package provides state-of-the-art distributed hyperparameter optimizers (HPO) where trials can be evaluated with several backend option
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.
Blitz - Bayesian Layers in Torch Zoo BLiTZ is a simple and extensible library to create Bayesian Neural Network Layers (based on whats proposed in Wei
inklayers is a command line program that exports layers from an SVG file.
inklayers is a command line program that exports layers from an SVG file. It can be used to create slide shows by editing a single SVG file.
A concept I came up which ditches the idea of "layers" in a neural network.
Dynet A concept I came up which ditches the idea of "layers" in a neural network. Install Copy Dynet.py to your project. Run the example Install matpl
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.
Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti
This is the official repository for our paper: ''Pruning Self-attentions into Convolutional Layers in Single Path''.
Pruning Self-attentions into Convolutional Layers in Single Path This is the official repository for our paper: Pruning Self-attentions into Convoluti
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear
Bayesian Additive Regression Trees For Python
BartPy Introduction BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1]. Reasons to use BART
TransMorph: Transformer for Medical Image Registration
TransMorph: Transformer for Medical Image Registration keywords: Vision Transformer, Swin Transformer, convolutional neural networks, image registrati
zeus is a Python implementation of the Ensemble Slice Sampling method.
zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Bl
Bayesian Generative Adversarial Networks in Tensorflow
Bayesian Generative Adversarial Networks in Tensorflow This repository contains the Tensorflow implementation of the Bayesian GAN by Yunus Saatchi and
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Kalman and Bayesian Filters in Python Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written
This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"
Prior-RObust Bayesian Optimization (PROBO) Introduction, TOC This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our
Python package for causal inference using Bayesian structural time-series models.
Python Causal Impact Causal inference using Bayesian structural time-series models. This package aims at defining a python equivalent of the R CausalI
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
DoWhy | An end-to-end library for causal inference Amit Sharma, Emre Kiciman Introducing DoWhy and the 4 steps of causal inference | Microsoft Researc
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
NNI Doc | 简体中文 NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture
Dragonfly is an open source python library for scalable Bayesian optimisation.
Dragonfly is an open source python library for scalable Bayesian optimisation. Bayesian optimisation is used for optimising black-box functions whose
Keras Implementation of The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation by (Simon Jégou, Michal Drozdzal, David Vazquez, Adriana Romero, Yoshua Bengio)
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation: Work In Progress, Results can't be replicated yet with the m
Recursive Bayesian Networks
Recursive Bayesian Networks This repository contains the code to reproduce the results from the NeurIPS 2021 paper Lieck R, Rohrmeier M (2021) Recursi
Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"
GINC small-scale in-context learning dataset GINC (Generative In-Context learning Dataset) is a small-scale synthetic dataset for studying in-context
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.
causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B
Generalized and Efficient Blackbox Optimization System.
OpenBox Doc | OpenBox中文文档 OpenBox: Generalized and Efficient Blackbox Optimization System OpenBox is an efficient and generalized blackbox optimizatio
Framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample resolution
Sample-specific Bayesian Networks A framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample or per-patient re
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces
This repository contains source code for the paper Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces a
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's algorithm.
Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil
This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.
Dugh-NeurIPS-2021 This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroi
Bayesian Optimization Library for Medical Image Segmentation.
bayesmedaug: Bayesian Optimization Library for Medical Image Segmentation. bayesmedaug optimizes your data augmentation hyperparameters for medical im
Open source single image super-resolution toolbox containing various functionality for training a diverse number of state-of-the-art super-resolution models. Also acts as the companion code for the IEEE signal processing letters paper titled 'Improving Super-Resolution Performance using Meta-Attention Layers’.
Deep-FIR Codebase - Super Resolution Meta Attention Networks About This repository contains the main coding framework accompanying our work on meta-at
The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper.
Intermdiate layer matters - SSL The official repository for "Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning" paper. Downl
a reccurrent neural netowrk that when trained on a peice of text and fed a starting prompt will write its on 250 character text using LSTM layers
RNN-Playwrite a reccurrent neural netowrk that when trained on a peice of text and fed a starting prompt will write its on 250 character text using LS
Implementation of several Bayesian multi-target tracking algorithms, including Poisson multi-Bernoulli mixture filters for sets of targets and sets of trajectories. The repository also includes the GOSPA metric and a metric for sets of trajectories to evaluate performance.
This repository contains the Matlab implementations for the following multi-target filtering/tracking algorithms: - Folder PMBM contains the implemen
Dynamica causal Bayesian optimisation
Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th
Building and deploying AWS Lambda Shared Layers
AWS Lambda Shared Layers This repository is hosting the code from the following blog post: AWS Lambda & Shared layers for Python. The goal of this rep
ConformalLayers: A non-linear sequential neural network with associative layers
ConformalLayers: A non-linear sequential neural network with associative layers ConformalLayers is a conformal embedding of sequential layers of Convo