Papers with Code 아카이브

Uncertainty Quantification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/slimgroup/Software.SEG2020

공식 코드

Uncertainty quantification in imaging and automatic horizon tracking: a Bayesian deep-prior based approach

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

공식 코드

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

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

공식 코드

ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems

https://github.com/slimgroup/velocity_continuation_with_fnos

공식 코드

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

https://github.com/smartyfh/llm-uncertainty-bench

공식 코드

Benchmarking LLMs via Uncertainty Quantification

https://github.com/snap-stanford/conformalized-gnn

공식 코드

Uncertainty Quantification over Graph with Conformalized Graph Neural Networks

https://github.com/soumickmj/pulaski

공식 코드

PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

https://github.com/spoc-group/double_descent_uncertainty

공식 코드

On double-descent in uncertainty quantification in overparametrized models

https://github.com/spoc-group/expectation-consistency

공식 코드

Expectation consistency for calibration of neural networks

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

공식 코드

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

https://github.com/sslab-gatech/acon2

공식 코드

ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles

https://github.com/ssokota/spie

공식 코드

Simultaneous Prediction Intervals for Patient-Specific Survival Curves

https://github.com/stadlmax/Graph-Posterior-Network

공식 코드

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

https://github.com/stanfordasl/data-lifecycle-management

공식 코드

Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications

https://github.com/stanfordcbcl/blnm

공식 코드

Full-field surrogate modeling of cardiac function encoding geometric variability

https://github.com/stat-ml/nuq

공식 코드

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

https://github.com/stc-lab/on-traffic

공식 코드

ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors

https://github.com/stoledoc/DLGP-DR-Diagnosis

공식 코드

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

https://github.com/sttdanonymous/sttd

공식 코드

Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

https://github.com/suli24/edl-stormcast

공식 코드

Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events

https://github.com/sungbinlim/GBS

공식 코드

Neural Bootstrapper

https://github.com/sungbinlim/neuboots

공식 코드

Neural Bootstrapper

https://github.com/sunnyqywang/uncertainty

공식 코드

Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks

https://github.com/sunyumark/PnP-MonteCarlo

공식 코드

Provable Probabilistic Imaging using Score-Based Generative Priors

https://github.com/surinahn/xai-uncertainty-quantification

공식 코드

Uncertainty Quantification for Local Model Explanations Without Model Access

https://github.com/swamiviv/CLASP

공식 코드

Semantic uncertainty intervals for disentangled latent spaces

https://github.com/swing-research/conditional-trumpets

공식 코드

Conditional Injective Flows for Bayesian Imaging

https://github.com/swing-research/scattering_injective_prior

공식 코드

Deep Injective Prior for Inverse Scattering

https://github.com/swing-research/trumpets

공식 코드

Trumpets: Injective Flows for Inference and Inverse Problems

https://github.com/sydney-machine-learning/BayesianAutoencoders

공식 코드

Revisiting Bayesian Autoencoders with MCMC

https://github.com/sydney-machine-learning/BayesianGraphNeuralNetworks

공식 코드

Bayesian graph convolutional neural networks via tempered MCMC

https://github.com/sydney-machine-learning/bayesianneuralnetworks-mcmc-tutorial

공식 코드

Bayesian neural networks via MCMC: a Python-based tutorial

https://github.com/sydney-machine-learning/quantiledeeplearning

공식 코드

Quantile deep learning models for multi-step ahead time series prediction

https://github.com/sydney-machine-learning/surrogate-assisted-parallel-tempering

공식 코드

Surrogate-assisted parallel tempering for Bayesian neural learning

https://github.com/syerramilli/lvgp-bayes

공식 코드

Fully Bayesian inference for latent variable Gaussian process models

https://github.com/sylydya/a-kernel-expanded-stochastic-neural-network

공식 코드

A Kernel-Expanded Stochastic Neural Network

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