https://github.com/meta-inf/bayes_icl
공식 코드Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
Papers with Code 아카이브
Bayesian Inference
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 2
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/meta-inf/qbdiv
공식 코드https://github.com/mgong2/DA_Infer
공식 코드Domain Adaptation as a Problem of Inference on Graphical Models
https://github.com/mgorinova/SlicStan
공식 코드Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
https://github.com/mgorinova/autoreparam
공식 코드https://github.com/mi2g/accelerated-langevin-imla
공식 코드Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
https://github.com/michelemancusi/LQVAE-separation
공식 코드Unsupervised Source Separation via Bayesian Inference in the Latent Domain
https://github.com/mikekatz04/eryn
공식 코드https://github.com/mikkelpm/het_agents_bayes
공식 코드Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data
https://github.com/mingzhang-yin/SIVI
공식 코드https://github.com/minyoungkim21/bayesdll
공식 코드https://github.com/minyoungkim21/niwmeta
공식 코드A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning
https://github.com/mjarvenpaa/parallel-GP-SL
공식 코드Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
https://github.com/mkomod/vi-per
공식 코드https://github.com/ml-gsai/bfn-solver
공식 코드Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations
https://github.com/ml-research/slash
공식 코드https://github.com/mlaves/bayesian-temperature-scaling
공식 코드Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
https://github.com/mlaves/well-calibrated-regression-uncertainty
공식 코드Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging
https://github.com/mlcolab/psi-kt
공식 코드Predictive, scalable and interpretable knowledge tracing on structured domains
https://github.com/mlglobalhealth/aggvae
공식 코드https://github.com/mlglobalhealth/pi-vae
공식 코드$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
https://github.com/mlisicki/neuralkernelbandits
공식 코드Empirical analysis of representation learning and exploration in neural kernel bandits
https://github.com/mmjing/variational_model_perturbation
공식 코드Variational Model Perturbation for Source-Free Domain Adaptation
https://github.com/modichirag/gsm-vi
공식 코드https://github.com/mpfarrho/tvp-qr
공식 코드Modeling tail risks of inflation using unobserved component quantile regressions
https://github.com/mrc-ide/covid-sim
공식 코드https://github.com/mspakkanen/integral-equations
공식 코드Unifying incidence and prevalence under a time-varying general branching process
https://github.com/mstaib/stochastic-barycenter-code
공식 코드https://github.com/mtezzele/digital-twin-shm
공식 코드https://github.com/mullachv/MLExp
공식 코드https://github.com/naitongchen/sparse-hamiltonian-flows
공식 코드https://github.com/nargesiPSH/Folded-Hamiltonian-Monte-Carlo
공식 코드A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare Data
https://github.com/nathanaelbosch/pick-and-mix
공식 코드Pick-and-Mix Information Operators for Probabilistic ODE Solvers
https://github.com/neilspencer/LatentPositionsMCMC
공식 코드https://github.com/networkslab/rnn_flow
공식 코드RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
https://github.com/neurolife77/drffit_paper
공식 코드A Metaheuristic for Amortized Search in High-Dimensional Parameter Spaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.