Papers with Code 아카이브
Bayesian Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 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/mukehvier/tensorflow-prob
https://github.com/mvaldenegro/keras-uncertainty
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/nathanaelbosch/ODEFilters.jl
Pick-and-Mix Information Operators for Probabilistic ODE Solvers
https://github.com/nawta/GSoC_deeplense_assignment
https://github.com/neurips2020submission7954/numpyro
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
https://github.com/ngiann/ApproximateVI.jl
https://github.com/nicola-decao/s-vae
https://github.com/nicola-decao/s-vae-tf
https://github.com/nicopao/CGM_prediction_data
MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas
https://github.com/nomercy77/Implementing-Bayesian-CNN
https://github.com/noob-life/VAE-for-Collaborative-Filtering-Pytorch
https://github.com/numfah23/VAE
https://github.com/omacshane/basicbayesDL
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/onurboyar/RecommenderSystems
https://github.com/ostadabbas/Machine-Learning-for-Precise-Optical-Wavelength-Estimation
https://github.com/ostadabbas/Machine-learning-for-nanomaterial-based-optical-wavelength-estimation
https://github.com/panditanvita/BTCpredictor
https://github.com/pankajb64/bayescnn
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/parikhs97/Predicting-Bitcoin-Price-Variations
https://github.com/piEsposito/blitz-bayesian-deep-learning
https://github.com/plai-group/pyprob
Inference Compilation and Universal Probabilistic Programming
https://github.com/pmshst/A-Real-time-System-of-Bitcoin-Price-Prediction
https://github.com/pourmalek/CovidVisualizedCountry
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
https://github.com/pourmalek/CovidVisualizedGlobal
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
https://github.com/pourmalek/covir2
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
https://github.com/prmody96/Predicting-Bitcoin-Price-Variations-using-Bayesian-Regression
https://github.com/probcomp/cgpm
https://github.com/probprog/pyprob
Inference Compilation and Universal Probabilistic Programming
https://github.com/probtorch/probtorch
https://github.com/pyro-ppl/pyro
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
https://github.com/pyy0715/vae-cf-pyorch
https://github.com/qsh-zh/cbp
Multi-marginal optimal transport and probabilistic graphical models
https://github.com/qshzh/cbp
Multi-marginal optimal transport and probabilistic graphical models
https://github.com/rajatdge/Bitcoin-Price-Prediction
https://github.com/rajguhaniyogi/Bayesian-Conditional-Density-Filtering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.