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/czgdp1807/bayesiandl
https://github.com/damast93/gaussianinfer
Compositional Semantics for Probabilistic Programs with Exact Conditioning
https://github.com/danielkelshaw/RadialBNN
https://github.com/danielkelshaw/WeightUncertainty
https://github.com/danrugeles/Heron
SAME but Different: Fast and High-Quality Gibbs Parameter Estimation
https://github.com/david-rundel/tabpfn_iml
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/davidreiman/snl
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
https://github.com/dennysemko/VectorizedBayesByBackprop
https://github.com/dflemin3/approxposterior
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
https://github.com/dhruvpatel108/GANPriors
https://github.com/dilinwang820/Stein-Variational-Gradient-Descent
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
https://github.com/elfi-dev/elfi
https://github.com/elizavetasemenova/priorvae
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
https://github.com/elizqian/balancing-bayesian-inference
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
https://github.com/enalisnick/stick-breaking_dgms
https://github.com/erickgalinkin/dropout_privacy
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/eth-cscs/abcpy
Score Matched Neural Exponential Families for Likelihood-Free Inference
https://github.com/fagonzalezo/qmc
https://github.com/felix-laumann/Bayesian_CNN_ContinualLearning
https://github.com/feynmanliang/dist-svgd
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
https://github.com/findaheng/Scenic
Scenic: A Language for Scenario Specification and Scene Generation
https://github.com/fournierlouis/synaptic_sampling_rbm
https://github.com/fraziezr/Machine_Learning_Final_Project_Team_12
https://github.com/futoshi-futami/Robust_VI
https://github.com/gbernstein6/private_bayesian_expfam
Differentially Private Bayesian Inference for Exponential Families
https://github.com/geoffroeder/iwae
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
https://github.com/giannifranchi/LP_BNN
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
https://github.com/google/edward2
Simple, Distributed, and Accelerated Probabilistic Programming
https://github.com/google/wide_bnn_sampling
https://github.com/gpapamak/bayesian_neural_networks_demo
https://github.com/gpapamak/distilling_model_knowledge
https://github.com/gtegner/hyper-gan
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/hansungj/BayesianNeuralNetworkDiscretizedGibbsSampler
https://github.com/hasteck/EDLAE_NeurIPS2020
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.