https://github.com/stephengreen/lfi-gw
Complete parameter inference for GW150914 using deep learning
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/storopoli/Bayesian-Julia
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
https://github.com/sveinnpalsson/semivaebrats
https://github.com/sydney-machine-learning/pingala
Surrogate-assisted parallel tempering for Bayesian neural learning
https://github.com/tabpfn-anonym/tabpfnanonym
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/taohu88/BayesianML
Stochastic Gradient Descent as Approximate Bayesian Inference
https://github.com/taranO/IB-semi-supervised-classification
https://github.com/tejasai97/BitCoinPricePrediction
https://github.com/tennisonliu/bayesian-neural-network
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits
https://github.com/tensorflow/probability
https://github.com/thegregyang/NNspectra
https://github.com/themisbo/Rule-based-Evol-Bayesian-regr
https://github.com/themisbo/auto-Rule-based-Bayesian-regr
https://github.com/tkusmierczyk/correcting_approximate_bayesian_predictions
https://github.com/treforevans/uci_datasets
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
https://github.com/trevorcampbell/bayesian-coresets
https://github.com/trevorcampbell/quasi-newton-coresets-experiments
https://github.com/trungnt13/odin-ai
https://github.com/tushar-agarwal2909/Bayes_by_Backprop-TF
https://github.com/tyxe-bdl/tyxe
https://github.com/ubc-stat-ml/bl-vpt
https://github.com/ucl-exoplanets/ADC2023-baseline
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/umich-curly/bki_ros
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
https://github.com/uncharted-technologies/risk-and-uncertainty
Estimating Risk and Uncertainty in Deep Reinforcement Learning
https://github.com/vardigroup/DPMC
https://github.com/victor-armegioiu/Learning-Bayesian-Priors
https://github.com/victor-armegioiu/Meta-Learning-Bayesian-Priors
https://github.com/wsgharvey/pyro-infcomp-examples
Inference Compilation and Universal Probabilistic Programming
https://github.com/wwrechard/random-tree-parallel-MCMC
https://github.com/xandaschofield/locally_private_bpf_icml19
https://github.com/xiayzh/MH-MDGM
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
https://github.com/xinshi-chen/MetaParticleFlow
https://github.com/xinshi-chen/ParticleFlowBayesRule
https://github.com/xpitfire/symbolicai
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.