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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/esennesh/dcpc_paper
공식 코드Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
https://github.com/esennesh/ppc_experiments
공식 코드Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm
https://github.com/ethanluoyc/sympais
공식 코드Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
https://github.com/extensityai/benchmark
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
https://github.com/facebookresearch/blackbox-coresets-vi
공식 코드https://github.com/facebookresearch/label_dp_antipodes
공식 코드https://github.com/facebookresearch/lightweight-inference-compilation
공식 코드Accelerating Metropolis-Hastings with Lightweight Inference Compilation
https://github.com/facebookresearch/pplbench
공식 코드PPL Bench: Evaluation Framework For Probabilistic Programming Languages
https://github.com/ferosas/bayesianatheart
공식 코드https://github.com/ferreirafabio80/gfa
공식 코드A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets
https://github.com/filipnovicky/Bistable_Perception
공식 코드https://github.com/fjssharpsword/BayesianRS
공식 코드Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
https://github.com/fnauman/ML_alpha2
공식 코드https://github.com/fortuinlab/la-nam
공식 코드https://github.com/fortuinlab/swag-lora
공식 코드Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
https://github.com/framartin/transferable-bnn-adv-ex
공식 코드Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
https://github.com/francois-rozet/amnre
공식 코드Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference
https://github.com/frederikwarburg/laplaceae
공식 코드Laplacian Autoencoders for Learning Stochastic Representations
https://github.com/fshp971/BIF
공식 코드https://github.com/fshp971/mcmc-unlearning
공식 코드https://github.com/fzaiser/genfer
공식 코드https://github.com/fzaiser/nonparametric-hmc
공식 코드https://github.com/gabegrand/world-models
공식 코드https://github.com/gabrielvc/mcg_diff
공식 코드Monte Carlo guided Diffusion for Bayesian linear inverse problems
https://github.com/ganondox/bayesian-codenames
공식 코드Improving Cooperation in Language Games with Bayesian Inference and the Cognitive Hierarchy
https://github.com/gchowell/bayesianfitforecast
공식 코드https://github.com/gelman/ep-stan
공식 코드Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
https://github.com/gerdm/weighted-likelihood-filter
공식 코드https://github.com/ggong369/mbnn
공식 코드Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
https://github.com/gitter-lab/ssps
공식 코드https://github.com/giuliotrigila/moonexample
공식 코드A generative flow for conditional sampling via optimal transport
https://github.com/google-research/cascades
공식 코드https://github.com/google-research/google-research
공식 코드How Good is the Bayes Posterior in Deep Neural Networks Really?
https://github.com/google-research/google-research/tree/master/automatic_structured_vi
공식 코드https://github.com/google-research/recsim_ng
공식 코드RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.