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Bayesian Inference

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보존된 평가 기록: 2

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://github.com/LehengTHU/Agent4Rec

Variational Autoencoders for Collaborative Filtering

https://github.com/MIC-DKFZ/TractSeg

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

https://github.com/MINGUKKANG/CVAE

Semi-Supervised Learning with Deep Generative Models

https://github.com/MJXWANG/AUKAI-demo

From Understanding the World to Intervening in It: A Unified Multi-Scale Framework for Embodied Cognition

https://github.com/MS-Mind/MS-Code-08/tree/main/NFNet

Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Sequential_Neural_Likelihood

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

https://github.com/Medabid1/Bayes-by-backprob

Weight Uncertainty in Neural Networks

https://github.com/MehdiChelh/Zoo

Weight Uncertainty in Neural Networks

https://github.com/Mind23-2/MindCode-152

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

https://github.com/Mind23-2/MindCode-5/tree/main/RAOD

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

https://github.com/MindSpore-paper-code-2/code2/tree/main/RAOD

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

https://github.com/MindSpore-scientific-2/code-1/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-11/tree/main/SVGD

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

https://github.com/MindSpore-scientific-2/code-11/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-2/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-8/tree/main/SVGD

Riemannian Stein Variational Gradient Descent for Bayesian Inference

https://github.com/MindSpore-scientific-2/code-8/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-9/tree/main/iwae

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

https://github.com/MiuGod0126/Bayes_backprop_paddle

Weight Uncertainty in Neural Networks

https://github.com/NajibYavari/DD2412

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/NamCyan/RecSys-VAE4CF

Variational Autoencoders for Collaborative Filtering

https://github.com/Neronjust2017/BayesianCNN

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

https://github.com/NicholasFarrow/GalacticDNSMass

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

https://github.com/PreferredAI/cornac/tree/master/cornac/models/vaecf

Variational Autoencoders for Collaborative Filtering

https://github.com/Priesemann-Group/covid19_inference

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

https://github.com/ProcessMonitoringStellenboschUniversity/IFAC-VAE-Imputation

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

https://github.com/SamuelGuilluy/Bayesian_ML_SWAG

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/SourangshuGhosh/BTCpredictor

Bayesian regression and Bitcoin

https://github.com/SourangshuGhosh/BitCoinPricepredictor

Bayesian regression and Bitcoin

https://github.com/SulemanKhurram/ThesisExperiments

Weight Uncertainty in Neural Networks

https://github.com/SuperKam91/bnn

Weight Uncertainty in Neural Networks

https://github.com/SuperKam91/gns

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

https://github.com/Tan-Furukawa/badzupa

Laplace approximation for logistic Gaussian process density estimation and regression

https://github.com/TeaPearce/Anchored_Ens_RL_Explore

Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning

https://github.com/TeaPearce/RL_Cart_Pole_Speed_Run

Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning

https://github.com/Tessii/Deep_Learning_Project_DTU

Semi-Supervised Learning with Deep Generative Models

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