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Bayesian Inference

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하위 태스크: 1
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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/ThirstyScholar/bayes-by-backprop

Weight Uncertainty in Neural Networks

https://github.com/TimmHess/OCDVAEContinualLearning

Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition

https://github.com/TuringLang/Bijectors.jl

Automatic Differentiation Variational Inference

https://github.com/UnofficialJuliaMirror/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

https://github.com/UnofficialJuliaMirrorSnapshots/AugmentedGaussianProcesses.jl-38eea1fd-7d7d-5162-9d08-f89d0f2e271e

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

https://github.com/UnofficialJuliaMirrorSnapshots/Bijectors.jl-76274a88-744f-5084-9051-94815aaf08c4

Automatic Differentiation Variational Inference

https://github.com/VBayesLab/Manifold-VB

Variational Bayes on Manifolds

https://github.com/VietTran86/PCA_MUSIC

Bayesian inference for PCA and MUSIC algorithms with unknown number of sources

https://github.com/Waino/hnmt

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/WayneDW/Contour-Stochastic-Gradient-Langevin-Dynamics

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

https://github.com/Windxy/BayesianCNN-Based-on-Paddle

Weight Uncertainty in Neural Networks

https://github.com/YeongHyeon/SWA-Gaussian-TF2

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/ZezhouCheng/GP-DIP

A Bayesian Perspective on the Deep Image Prior

https://github.com/ZoneTsuyoshi/pyassim

Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter

https://github.com/aadityasingh/icl-dynamics

The Transient Nature of Emergent In-Context Learning in Transformers

https://github.com/abdelrahmansaud/vLSTM

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

https://github.com/abdulmajid-murad/deep_probabilistic_forecast

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/acr42/Neural-Variational-Knowledge-Graphs

TensorFlow Distributions

https://github.com/activatedgeek/stein-gradient

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

https://github.com/agnesdeng/misle

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

https://github.com/akashsrikanth2310/Predicting-Bitcoin-Price-Variations-using-Bayesian-Regression-

Bayesian regression and Bitcoin

https://github.com/aleatory-science/numpyro

Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

https://github.com/alegresor/vitruncate

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

https://github.com/allenbeau/pfedbayes

Personalized Federated Learning via Variational Bayesian Inference

https://github.com/allnightlight/ConditionalWassersteinAutoencoderPoweredBySinkhornDistance

Sinkhorn AutoEncoders

https://github.com/amoussawi/recoder

Variational Autoencoders for Collaborative Filtering

https://github.com/amspector100/deblendingstarfields

Variational Inference for Deblending Crowded Starfields

https://github.com/anhinga/fluid

Dataflow Matrix Machines as a Generalization of Recurrent Neural Networks

https://github.com/anonym0305/cov19inf

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

https://github.com/anonyme20/nips20

Semi-Supervised Learning with Deep Generative Models

https://github.com/anonymousest/DBSN

Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure

https://github.com/anonymouspap/latte_recsys

Variational Autoencoders for Collaborative Filtering

https://github.com/apratimbhattacharyya18/seg_pred

Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods

https://github.com/aredier/monte_carlo_dropout

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

https://github.com/aripakman/amortized_community_detection

Neural Clustering Processes

https://github.com/aripakman/attentive_clustering_processes

Neural Clustering Processes

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