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Bayesian Inference

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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/s-takabe/DUSVGD

공식 코드

Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding

https://github.com/sandialabs/bcnn

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We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty

https://github.com/sandialabs/qnvb

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Projective Integral Updates for High-Dimensional Variational Inference

https://github.com/sashrikap/context-steering

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CoS: Enhancing Personalization and Mitigating Bias with Context Steering

https://github.com/sbi-dev/sbi

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sbi reloaded: a toolkit for simulation-based inference workflows

https://github.com/scc-usc/ReCOVER-COVID-19

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Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model

https://github.com/schmidtjonathan/pnmol-experiments

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Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations

https://github.com/schyanzafar/DiSC

공식 코드

Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inference

https://github.com/sciml-fl/bra-fl

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Bayesian Robust Aggregation for Federated Learning

https://github.com/sdatkinson/BEBO

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Bayesian task embedding for few-shot Bayesian optimization

https://github.com/sebbarb/time_aware_attention

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Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk

https://github.com/sg5g10/hmm

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Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs

https://github.com/sgoldt/reconstructing_memories

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Bayesian reconstruction of memories stored in neural networks from their connectivity

https://github.com/sgttwld/two-kinds-of-free-energy--notebooks

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The Two Kinds of Free Energy and the Bayesian Revolution

https://github.com/sheinkmana/ArchitectureofBNNs

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Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks

https://github.com/sheinkmana/V_bowtie_NN

공식 코드

Variational Bayesian Bow tie Neural Networks with Shrinkage

https://github.com/simonrsantana/AADM

공식 코드

Adversarial $α$-divergence Minimization for Bayesian Approximate Inference

https://github.com/sinanyildirim/bayesian_dp_dist_lr

공식 코드

Differentially Private Distributed Bayesian Linear Regression with MCMC

https://github.com/sisl/distributed_admm_sampler

공식 코드

Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers

https://github.com/sisl/modelbasedvalidationinference

공식 코드

Model-based Validation as Probabilistic Inference

https://github.com/skezle/bayes_cl_posterior

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On Sequential Bayesian Inference for Continual Learning

https://github.com/slimgroup/InvertibleNetworks.jl

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Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/slimgroup/Software.SEG2021

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Learning by example: fast reliability-aware seismic imaging with normalizing flows

https://github.com/slimgroup/deep_inference_with_tasks

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Deep Bayesian inference for seismic imaging with tasks

https://github.com/slimgroup/reliableavi.jl

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Reliable amortized variational inference with physics-based latent distribution correction

https://github.com/slinderman/pyglm

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Bayesian latent structure discovery from multi-neuron recordings

https://github.com/slinderman/pyhawkes

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Scalable Bayesian Inference for Excitatory Point Process Networks

https://github.com/soon91jae/uvwsd

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Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information

https://github.com/spectraldani/pai

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Parallel MCMC Without Embarrassing Failures

https://github.com/spm/spm

공식 코드

BSD: a Bayesian framework for parametric models of neural spectra

https://github.com/sremes/wishart-gibbs-kernel

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A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

https://github.com/sri-csl/bayesadapt

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Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference

https://github.com/ssi-research/bayesagg_mtl

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Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning

https://github.com/stablemarkets/intro_bayesian_causal

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A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches

https://github.com/stefanradev93/AIAgainstCorona

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OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany

https://github.com/stefanradev93/BayesFlow

공식 코드

BayesFlow: Amortized Bayesian Workflows With Neural Networks

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