https://github.com/arneschmidt/bayesian_deep_learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Papers with Code 아카이브
Bayesian Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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://github.com/arodriguezca/uncertainty-ts-forecasting
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/asharakeh/probdet
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/aurelio-amerio/concretedropout
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/automl/tabpfn
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/babylonhealth/multiverse
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
https://github.com/bachtranxuan/GCTM
https://github.com/bachtranxuan/TPS
https://github.com/badlands-model/Bayeslands_Workshop
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
https://github.com/bat/MGVI.jl
https://github.com/bat/MGVInference.jl
https://github.com/bayesflow-org/bayesflow
BayesFlow: Learning complex stochastic models with invertible neural networks
https://github.com/bccp/DeepUQ
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
https://github.com/beauCoker/bayesian_neural_networks
https://github.com/bgrimstad/sgvb-torch
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study
https://github.com/bhaddow/dev-nematus
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
https://github.com/blacKitten13/Dropout-for-Model-Uncertainty
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/bradleygramhansen/pyfo
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
https://github.com/bynchang/semi-supervised-VAE
https://github.com/calvinmccarter/kditransform
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/candy4869/2014
https://github.com/cdebeunne/uncertainties_CNN
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/chandu-97/BayesByBackprop
https://github.com/chenxy99/stein-variational-gradient-descent
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
https://github.com/christabella/bruno
https://github.com/christiando/SGCP_Inference
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
https://github.com/chrvt/denoising-normalizing-flow
https://github.com/clarken92/ConsistencySSL
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
https://github.com/clelidm/MinCompSpin_Greedy
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary Order
https://github.com/clelidm/MinimallyComplex_SpinModels
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary Order
https://github.com/clinicalml/structuredinference
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
https://github.com/code-implementation1/Code6/tree/main/RAOD
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
https://github.com/cognitivemodeling/finn
https://github.com/conormdurkan/lfi
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
https://github.com/cpark321/bayesian-neural-networks
https://github.com/cpark321/uncertainty-deep-learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.