Python Bayesian-layers Resources

Zoobot Zoobot classifies galaxy morphology with deep learning. This code will let you: Reproduce and improve the Galaxy Zoo DECaLS automated classific

Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks This is the master thesi

Bayesian Optimisation Research This directory contains official implementations for Bayesian optimisation works developped by Huawei R&D, Noah's Ark L

Unsupervised Depth Completion with Calibrated Backprojection Layers PyTorch implementation of Unsupervised Depth Completion with Calibrated Backprojec

PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)

GMM-CNN This code package implements the modeling of CNN layers activity with Gaussian mixture model and Inference Graphs visualization technique from

vmgp This is the repository of Vivek Myers and Nikhil Sardana for our CS 330 final project, Bayesian Meta-Learning Through Variational Gaussian Proces

BoMb-OT Python3 implementation of the papers On Transportation of Mini-batches: A Hierarchical Approach and Improving Mini-batch Optimal Transport via

Uncertainty Baselines The goal of Uncertainty Baselines is to provide a template for researchers to build on. The baselines can be a starting point fo

Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning Update (September 18th, 2021) A supporting document de

ReLU-GP Residual (RGPR) This repository contains code for reproducing the following NeurIPS 2021 paper: @inproceedings{kristiadi2021infinite, title=

BO-GP Bayesian optimization based on Gaussian processes (BO-GP) for CFD simulations. The BO-GP codes are developed using GPy and GPyOpt. The optimizer

HumBugDB Acoustic mosquito detection with Bayesian Neural Networks. Extract audio or features from our large-scale dataset on Zenodo. This repository

A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

unfoldedVBA Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution This repository contains the Pytorch implementation of the unrolled

Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc

Uncertainty-based OOD detection Exploring the link between uncertainty estimates obtained by "exact" Bayesian inference and out-of-distribution (OOD)

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

BayesFGM Paper: Jiajing Niu, Andrew Brown. Bayesian regularization for functional graphical models. Requirements R version 3.6.3 and up Python 3.6 and

Surgically remove layers from a Docker image (with a chainsaw)

BASTA: BAyesian STellar Algorithm Current stable version: v1.0 Important note: BASTA is developed for Python 3.8, but Python 3.7 should work as well.

Website, Tutorials, and Docs    Uncertainty Toolbox A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualizatio

Code release for "Bayesian Compression for Deep Learning" In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

Dear PyGui Extensions: A collection of useful tools, abstractions, and simplification layers built with/for Dear PyGui users.

fourier-bayesian-sv-estimation Fourier-Bayesian estimation of stochastic volatility models Code used to run the numerical examples of "Bayesian Approa

Case studies with Bayesian methods

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

FreezeOut A simple technique to accelerate neural net training by progressively freezing layers. This repository contains code for the extended abstra

DEMix This repository contains modeling utilities for "DEMix Layers: Disentangling Domains for Modular Language Modeling" (Gururangan et. al, 2021). T

Bayesian Algorithm Execution (BAX) Code for the paper: Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mut

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition (PyTorch) Paper: https://arxiv.org/abs/2105.01883 Citation: @

OrthNet TensorFlow, PyTorch and Numpy layers for generating multi-dimensional Orthogonal Polynomials 1. Installation 2. Usage 3. Polynomials 4. Base C

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Bonsai is a wrapper for the XGBoost and Catboost model training pipelines that leverages Bayesian optimization for computationally efficient hyperparameter tuning.

Compare outputs of Wasserstein GANs between TensorFlow vs Pytorch This is our testing module for the implementation of improved WGAN in Pytorch Prereq

Spectral Tensor Train Parameterization of Deep Learning Layers This repository is the official implementation of our AISTATS 2021 paper titled "Spectr

Dangers of Bayesian Model Averaging under Covariate Shift This repository contains the code to reproduce the experiments in the paper Dangers of Bayes

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving Abstract In this paper, we introduce SalsaNext f

PosteriorStacker Combines Bayesian analyses from many datasets. Introduction Method Tutorial Output plot and files Introduction Fitting a model to a d

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

RepMLP RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition Released the code of RepMLP together with an example o

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs This is an implemetation of the paper Few-shot Relation Extraction via Baye

Bayesian optimization in JAX

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

Meta Language-Specific Layers in Multilingual Language Models This repo contains the source codes for our paper On Negative Interference in Multilingu

ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, data storage, model checking, comparison and diagnostics

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

Bayesian Image Reconstruction using Deep Generative Models R. Marinescu, D. Moyer, P. Golland For technical inquiries, please create a Github issue. F

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems and automatically guides the design of experiment to be evaluated.

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning Warning: This is a rapidly evolving research prototype.

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ZhuSuan is a Python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and

Pingouin is an open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. Some of its main features are listed below. F

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Bambi BAyesian Model-Building Interface in Python Overview Bambi is a high-level Bayesian model-building interface written in Python. It's built on to

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow

FilterPy - Kalman filters and other optimal and non-optimal estimation filters in Python. NOTE: Imminent drop of support of Python 2.7, 3.4. See secti

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

TensorFlow Probability TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFl

PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W

deck.gl | Website WebGL2-powered, highly performant large-scale data visualization deck.gl is designed to simplify high-performance, WebGL-based visua

Summit Summit is a set of tools for optimising chemical processes. We’ve started by targeting reactions. What is Summit? Currently, reaction optimisat

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Süsstrunk {Note: p

uMap project About uMap lets you create maps with OpenStreetMap layers in a minute and embed them in your site. Because we think that the more OSM wil

Infinitely Deep Bayesian Neural Networks with SDEs This library contains JAX and Pytorch implementations of neural ODEs and Bayesian layers for stocha

Note: This package is for use with GPFlow 1. For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind. GPflowOpt GP

Scikit-Optimize Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements

SafeOpt - Safe Bayesian Optimization This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also p

Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. PyPI (pip): $ pip install bayesian-optimizat

SMAC v3 Project Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). Ho

BoTorch is a library for Bayesian Optimization built on PyTorch. BoTorch is currently in beta and under active development! Why BoTorch ? BoTorch Prov

MXFusion | | | | Tutorials | Documentation | Contribution Guide MXFusion is a modular deep probabilistic programming library. With MXFusion Modules yo

PyVarInf PyVarInf provides facilities to easily train your PyTorch neural network models using variational inference. Bayesian Deep Learning with Vari

Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes

Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

CapsNet-Visualization For more information on capsule networks check out my Medium articles here and here. Setup Use pip to install the required pytho

auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

A Python library that helps data scientists to infer causation rather than observing correlation.

Kapre Keras Audio Preprocessors - compute STFT, ISTFT, Melspectrogram, and others on GPU real-time. Tested on Python 3.6 and 3.7 Why Kapre? vs. Pre-co

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. UltraOpt is a simple and efficient library to minimize expensive

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal

numpy-ml Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in NumPy? No? Install

Introduction neonrvm is an open source machine learning library for performing regression tasks using RVM technique. It is written in C programming la

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

Python package for Bayesian Machine Learning with scikit-learn API Installing & Upgrading package pip install https://github.com/AmazaspShumik/sklearn