https://github.com/tomcdonald/Deep-LFM
공식 코드Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features
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/tomekkorbak/pretraining-with-human-feedback
공식 코드https://github.com/tomwaka/ba-overparameterized-nonlinreg
공식 코드Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
https://github.com/treforevans/gp_grief
공식 코드Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
https://github.com/tripdancer0916/slow-reservoir
공식 코드Bayesian inference is facilitated by modular neural networks with different time scales
https://github.com/uber-research/vargp
공식 코드Variational Auto-Regressive Gaussian Processes for Continual Learning
https://github.com/uber/pyro
공식 코드https://github.com/umich-curly/bkidynamicsemanticmapping
공식 코드Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference
https://github.com/umich-curly/neuralbki
공식 코드Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
https://github.com/undark-lab/swyft
공식 코드Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
https://github.com/uu-sml/neurips2019-parameter-elimination
공식 코드https://github.com/vallis/truebayes
공식 코드Learning Bayesian posteriors with neural networks for gravitational-wave inference
https://github.com/vanderschaarlab/mlforhealthlabpub
공식 코드https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/causal_multitask_gaussian_processes_ite
공식 코드Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
https://github.com/vbayeslab/pmfvb
공식 코드https://github.com/vbayeslab/realrech
공식 코드https://github.com/vbayeslab/vbsl-wg
공식 코드Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
https://github.com/vectorinstitute/neuralkernelbandits
공식 코드Empirical analysis of representation learning and exploration in neural kernel bandits
https://github.com/vibujithan/macaw-2d
공식 코드https://github.com/vicgalle/distilled-self-critique
공식 코드Distilled Self-Critique of LLMs with Synthetic Data: a Bayesian Perspective
https://github.com/vicgalle/sgmcmc-force
공식 코드https://github.com/vicgalle/vis
공식 코드Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs
https://github.com/vislearn/trade
공식 코드TRADE: Transfer of Distributions between External Conditions with Normalizing Flows
https://github.com/vmasrani/qpaths_uai_2021
공식 코드q-Paths: Generalizing the Geometric Annealing Path using Power Means
https://github.com/vmboehm/pae-ablation
공식 코드https://github.com/waterism211/variational-median
공식 코드https://github.com/wazizian/onlinesampling.jl
공식 코드Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation
https://github.com/wearepal/qbold-vi
공식 코드https://github.com/willieneis/ProBO
공식 코드ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
https://github.com/wilson-ye-chen/sp-mcmc
공식 코드https://github.com/wiseodd/bayesian_ood_training
공식 코드https://github.com/wiseodd/last_layer_laplace
공식 코드Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
https://github.com/wjmaddox/drbayes
공식 코드https://github.com/wjmaddox/swa_gaussian
공식 코드https://github.com/wonyeol/static-analysis-for-support-match
공식 코드Towards Verified Stochastic Variational Inference for Probabilistic Programs
https://github.com/xinleiniu/latentoptimalpathsbayesiandp
공식 코드Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic Programming
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.