https://github.com/stefanradev93/cINN
공식 코드BayesFlow: Learning complex stochastic models with invertible neural networks
Papers with Code 아카이브
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://github.com/studying910/novi-dgp
공식 코드https://github.com/suchard-group/approximate_substitution_gradient_supplement
공식 코드Random-effects substitution models for phylogenetics via scalable gradient approximations
https://github.com/swing-research/trumpets
공식 코드Trumpets: Injective Flows for Inference and Inverse Problems
https://github.com/sydney-machine-learning/BayesianAutoencoders
공식 코드https://github.com/sydney-machine-learning/BayesianGraphNeuralNetworks
공식 코드Bayesian graph convolutional neural networks via tempered MCMC
https://github.com/sydney-machine-learning/bayesianneuralnetworks-mcmc-tutorial
공식 코드https://github.com/sydney-machine-learning/surrogate-assisted-parallel-tempering
공식 코드Surrogate-assisted parallel tempering for Bayesian neural learning
https://github.com/syerramilli/lvgp-bayes
공식 코드Fully Bayesian inference for latent variable Gaussian process models
https://github.com/tadsgroup/conditionalot2023
공식 코드https://github.com/taimurhassan/inc-inst-seg
공식 코드https://github.com/takuomatsubara/KSD-Bayes
공식 코드Robust Generalised Bayesian Inference for Intractable Likelihoods
https://github.com/takuomatsubara/discrete-fisher-bayes
공식 코드Generalised Bayesian Inference for Discrete Intractable Likelihood
https://github.com/takuyaisomura/reverse_engineering
공식 코드Triple equivalence for the emergence of biological intelligence
https://github.com/talesa/amci
공식 코드https://github.com/tensorflow/neural-structured-learning/tree/master/research/neural_clustering
공식 코드https://github.com/teojd/surrogate_da-hmc
공식 코드https://github.com/thecml/baysurv
공식 코드Efficient Training of Probabilistic Neural Networks for Survival Analysis
https://github.com/themisbo/Rule-based-Bayesian-regr
공식 코드https://github.com/themisbo/rule-based-evol-bayesian-learn
공식 코드https://github.com/theogf/AugmentedGaussianProcesses.jl
공식 코드https://github.com/thomfoster/treeQuadrature
공식 코드https://github.com/thu-ml/zhusuan
공식 코드https://github.com/tianyucodings/cdiff
공식 코드Conditional diffusions for amortized neural posterior estimation
https://github.com/tillahoffmann/summaries
공식 코드Unifying Summary Statistic Selection for Approximate Bayesian Computation
https://github.com/timlautk/lmc-atomi
공식 코드Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
https://github.com/timothygebhard/ml4ptp
공식 코드Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks
https://github.com/timrudner/S-FSVI
공식 코드Continual Learning via Sequential Function-Space Variational Inference
https://github.com/timrudner/fsvi
공식 코드Tractable Function-Space Variational Inference in Bayesian Neural Networks
https://github.com/timrudner/group-aware-priors
공식 코드Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
https://github.com/timxzz/abi_with_rebasin
공식 코드A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
https://github.com/timxzz/hit
공식 코드Trading Information between Latents in Hierarchical Variational Autoencoders
https://github.com/tingyuansen/statml
공식 코드https://github.com/tmichoel/bayonet
공식 코드Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
https://github.com/tml-tuebingen/gibbs-prior-diagnostic
공식 코드Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
https://github.com/tom-900/bayesian-semi-supervised-impedance-inversion
공식 코드Semi-supervised Impedance Inversion by Bayesian Neural Network Based on 2-d CNN Pre-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.