https://github.com/xujianscut/GPODE-DNF
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing Flows
Papers with Code 아카이브
Bayesian Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yamada-github-account/LearnBayesNN
https://github.com/yaringal/BayesianRNN
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
https://github.com/yaringal/DropoutUncertaintyExps
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/yburda/iwae
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
https://github.com/yliess86/Bayeformers
https://github.com/younggyoseo/vae-cf-pytorch
https://github.com/yueqiw/ncp-sort
https://github.com/yumaloop/dnn_hsic
https://github.com/zabaras/MH-MDGM
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
https://github.com/zhangyaoyuan/GAN-Simplification
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
https://github.com/zhaoxile/reproducible-tensor-completion-state-of-the-art
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination
https://github.com/zhoudoao-bayes/tf-probability
https://github.com/zoj613/polya-gamma
Bayesian inference for logistic models using Polya-Gamma latent variables
https://github.com/zqsh/cbp
Multi-marginal optimal transport and probabilistic graphical models
https://github.com/ztangent/Plinf.jl
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
https://gitlab.com/a-p-petrosyan/sspr
https://gitlab.com/ml-physics-unal/qcm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.