https://github.com/ksnxr/ssgrldndm
공식 코드Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics
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/kudlicka/paper-2019-probabilistic
공식 코드https://github.com/kumar-shridhar/PyTorch-BayesianCNN
공식 코드A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/lacerbi/infbench
공식 코드https://github.com/lacerbi/vbmc
공식 코드https://github.com/langosco/neural-variational-gradient-descent
공식 코드https://github.com/lanl/COVID-19-Predictions
공식 코드https://github.com/larsonlab/dnlinv
공식 코드Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstruction
https://github.com/laurencee9/structural_pertubation_detection
공식 코드Detecting structural perturbations from time series with deep learning
https://github.com/laurimi/ddcrp-gibbs
공식 코드Object proposal generation applying the distance dependent Chinese restaurant process
https://github.com/lava-lab/luiaard
공식 코드https://github.com/lazaratan/dyn-gfn
공식 코드DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
https://github.com/lbai-lab/push
공식 코드Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
https://github.com/leviyevalex/ssvn
공식 코드https://github.com/lfochamon/pdlmc
공식 코드https://github.com/likun-stat/xvae
공식 코드Flexible and efficient emulation of spatial extremes processes via variational autoencoders
https://github.com/lindermanlab/neymanscottprocesses.jl
공식 코드https://github.com/lingbinbian/hierarchicalbayesianmodelling
공식 코드https://github.com/lll6924/automatically-marginalized-mcmc
공식 코드Automatically Marginalized MCMC in Probabilistic Programming
https://github.com/lll6924/hamiltonian_lme
공식 코드Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
https://github.com/longinyu/hsivi
공식 코드Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration
https://github.com/longinyu/sivism
공식 코드https://github.com/lorenzodallamico/NishimoriBetheHessian
공식 코드Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs
https://github.com/lorenzorimella/pal
공식 코드https://github.com/louissharrock/coin-svgd
공식 코드Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
https://github.com/lucasmaystre/kickscore
공식 코드https://github.com/lucasmaystre/kickscore-kdd19
공식 코드https://github.com/luchinoprince/mixture_is
공식 코드Robust leave-one-out cross-validation for high-dimensional Bayesian models
https://github.com/lucieperrotta/temperature_calibration
공식 코드Practical calibration of the temperature parameter in Gibbs posteriors
https://github.com/lxing532/topic_variability
공식 코드https://github.com/lykaust15/DLBI
공식 코드https://github.com/ma-gilles/wilson_prior
공식 코드A Molecular Prior Distribution for Bayesian Inference Based on Wilson Statistics
https://github.com/ma921/basq
공식 코드Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
https://github.com/mackelab/delfi
공식 코드Flexible statistical inference for mechanistic models of neural dynamics
https://github.com/mackelab/fsGIF
공식 코드Inference of a mesoscopic population model from population spike trains
https://github.com/mackelab/gatsbi
공식 코드GATSBI: Generative Adversarial Training for Simulation-Based Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.