Papers with Code 아카이브

Bayesian Inference

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/probcomp/threedp3

공식 코드

3DP3: 3D Scene Perception via Probabilistic Programming

https://github.com/probml/bandits

공식 코드

Efficient Online Bayesian Inference for Neural Bandits

https://github.com/probml/rebayes

공식 코드

Low-rank extended Kalman filtering for online learning of neural networks from streaming data

https://github.com/probmods/webppl-daipp

공식 코드

Deep Amortized Inference for Probabilistic Programs

https://github.com/pyijiezhang/cpe-underfit

공식 코드

On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees

https://github.com/pyprob/pyprob

공식 코드

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

https://github.com/pyro-ppl/funsor

공식 코드

Functional Tensors for Probabilistic Programming

https://github.com/pyro-ppl/numpyro

공식 코드

Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro

https://github.com/qinglong-tian/tabpfn_study

공식 코드

TabPFN: One Model to Rule Them All?

https://github.com/qinglyu0828/softdropconnect

공식 코드

SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis

https://github.com/qjy415417122/sa-roundtrip

공식 코드

Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler

https://github.com/rafaol/batch-bore-with-guarantees

공식 코드

Batch Bayesian optimisation via density-ratio estimation with guarantees

https://github.com/ralphc1212/amortized-approximation-of-induced-distribution

공식 코드

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

https://github.com/ratschlab/repulsive_ensembles

공식 코드

Repulsive Deep Ensembles are Bayesian

https://github.com/rbouckaert/snapper

공식 코드

Bayesian inference of species trees using diffusion models

https://github.com/red-portal/mcmcsaem.jl

공식 코드

Stochastic Approximation with Biased MCMC for Expectation Maximization

https://github.com/reml-lab/URSABench

공식 코드

URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks

https://github.com/resuldagdanov/robot-learning-human-trust

공식 코드

Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales

https://github.com/rferguson22/gaussian-process

공식 코드

Data-driven Approach for Interpolation of Sparse Data

https://github.com/rgiordan/dadvipaper

공식 코드

Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box

https://github.com/ricardoborsoi/redsunn

공식 코드

Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks

https://github.com/richardli/SSJGL

공식 코드

Bayesian Joint Spike-and-Slab Graphical Lasso

https://github.com/rjnbaldock/nn_sample

공식 코드

Bayesian Neural Networks at Finite Temperature

https://github.com/roads/psiz

공식 코드

Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

https://github.com/robert-lieck/rbn

공식 코드

Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks

https://github.com/robinthibaut/skbel

공식 코드

A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area

https://github.com/rothkopflab/naba

공식 코드

Inverse decision-making using neural amortized Bayesian actors

https://github.com/roualdes/bridgestan

공식 코드

Variational Inference with Gaussian Score Matching

https://github.com/rpopov42/elbo_gaa

공식 코드

Analytical Approximation of the ELBO Gradient in the Context of the Clutter Problem

https://github.com/rubenohana/gibbs-diffusion

공식 코드

Listening to the Noise: Blind Denoising with Gibbs Diffusion

https://github.com/ruiqi-zhong/emnlp23-apel

공식 코드

Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL

https://github.com/ruqizhang/csgmcmc

공식 코드

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

https://github.com/ruqizhang/dpfastmh

공식 코드

DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference

https://github.com/rvandeghen/mda

공식 코드

Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillance

https://github.com/rycolab/evidence-probing

공식 코드

Probing as Quantifying Inductive Bias

https://github.com/s-sahoo/mulan

공식 코드

Diffusion Models With Learned Adaptive Noise

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.