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Bayesian Inference

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Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://www.github.com/google/neural-tangents

공식 코드

Neural Tangents: Fast and Easy Infinite Neural Networks in Python

https://github.com/2023-MindSpore-4/Code6/tree/main/RAOD

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

https://github.com/AI-HPC-Research-Team/GW

Complete parameter inference for GW150914 using deep learning

https://github.com/AI-for-Ocean-Science/ulmo

Probabilistic Autoencoder

https://github.com/AndreevP/FRCL

Functional Regularisation for Continual Learning with Gaussian Processes

https://github.com/Anou9531/Bayesian-CNN

Weight Uncertainty in Neural Networks

https://github.com/AstroGidi/PyDynamicaLC

MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

https://github.com/Battery-Intelligence-Lab/BayesianModelSelection

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

https://github.com/BerenMillidge/DeepActiveInference

Deep Active Inference as Variational Policy Gradients

https://github.com/Bmillidgework/DeepActiveInference_Code

Deep Active Inference as Variational Policy Gradients

https://github.com/COMP6248-Reproducability-Challenge/Reproduction-of-Probabilistic-binary-neural-networks

Variational Dropout and the Local Reparameterization Trick

https://github.com/ChangdeDu/DGMM

Sharing deep generative representation for perceived image reconstruction from human brain activity

https://github.com/Cognizant-Covid19Solutions/ProjectionModels

Inferring COVID-19 spreading rates and potential change points for case number forecasts

https://github.com/DPBayes/d3p

Differentially Private Variational Inference for Non-conjugate Models

https://github.com/DPBayes/dppp

Differentially Private Variational Inference for Non-conjugate Models

https://github.com/Daniil-Selikhanovych/bnn-vi

On the Expressiveness of Approximate Inference in Bayesian Neural Networks

https://github.com/DarkMachines/pyBAMBI

BAMBI: blind accelerated multimodal Bayesian inference

https://github.com/DennisReddyhoff/ContinualGP

Continual Multi-task Gaussian Processes

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Weight Uncertainty in Neural Networks

https://github.com/ElementAI/baal

DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks

https://github.com/Eungyu-Jin/Dropout_uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/FarzadP292/AMP-for-mismatched-matrix-estimation

Mismatched Estimation of rank-one symmetric matrices under Gaussian noise

https://github.com/GaganNarula/Bootstrap_particle_filter

Stochastic Gradient MCMC for Nonlinear State Space Models

https://github.com/GianluigiSilvestri/asvi

Automatic structured variational inference

https://github.com/HKUST-KnowComp/R-Net

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/HongleiXie/demo-CVAE

Semi-Supervised Learning with Deep Generative Models

https://github.com/HongtengXu/Hawkes-Process-Toolkit

A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering

https://github.com/Ian-Buckley/Probabilisitic-programming

An Introduction to Probabilistic Programming

https://github.com/IevaKazlauskaite/compositional_uncertainty

Compositional uncertainty in deep Gaussian processes

https://github.com/IntelLabs/bayesian-torch

Weight Uncertainty in Neural Networks

https://github.com/Ipsedo/TabPFN

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/JohannesBuchner/PyMultiNest

MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

https://github.com/Joshuaalbert/jaxns

MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

https://github.com/JulienNonin/auto-diff-variational-inference

Automatic Differentiation Variational Inference

https://github.com/KhurramPirov/bayesian_Inverse

Variational Reformulation of Bayesian Inverse Problems

https://github.com/LMikeH/ocbnn-lmikh

Weight Uncertainty in Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.