https://github.com/hhl60492/deep-kernel-transfer
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Papers with Code 아카이브
Uncertainty Quantification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/hoidn/ptychopinn
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/huyng/incertae
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/iinemo/llm-uncertainty-head
https://github.com/ixsluo/cond-cdvae
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
https://github.com/jelleman8/TractSeg
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/jun-cen/psl
Evidential Deep Learning to Quantify Classification Uncertainty
https://github.com/katiana22/GDM-PCE-surrogates
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
https://github.com/keanson/revisit-logistic-softmax
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
https://github.com/kenya-sk/mc_dropout_tensorflow
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/knc6/cdvae
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
https://github.com/kosyoshida/simple-keras
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/labmlai/annotated_deep_learning_paper_implementations
Evidential Deep Learning to Quantify Classification Uncertainty
https://github.com/lamantinushka/StructuredCovariance
https://github.com/lbasora/bayesrul
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics
https://github.com/learningmatter-mit/NeuralForceField
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
https://github.com/lmandl/separable-pi-deeponet
https://github.com/marcovirgolin/UncertaintyEstimationInDeepNets
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/matinmoezzi/ups_conformal_classification
Uncertainty Sets for Image Classifiers using Conformal Prediction
https://github.com/mit-acl/mppi_numba
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
https://github.com/ml-stat-sustech/torchcp
https://github.com/mrahtz/learning-from-human-preferences
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/mrazvan22/brain-coloring
Modeling and inference of spatio-temporal protein dynamics across brain networks
https://github.com/mroberto166/PinnsSub
https://github.com/mroberto166/nio
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
https://github.com/ms96590/-
Evidential Deep Learning to Quantify Classification Uncertainty
https://github.com/ms96590/pytorch-classification-uncertainty
Evidential Deep Learning to Quantify Classification Uncertainty
https://github.com/mvaldenegro/keras-uncertainty
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/niki-amini-naieni/instantcalibration
Instant Uncertainty Calibration of NeRFs Using a Meta-Calibrator
https://github.com/nisl-msu/adaptivesampling
Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation
https://github.com/oatml/semantic-entropy-probes
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
https://github.com/omacshane/basicbayesDL
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/peiyannn/mm-pde
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
https://github.com/polixir/OfflineRL
https://github.com/pycroscopy/atomai
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/razvanmarinescu/brain-coloring
Modeling and inference of spatio-temporal protein dynamics across brain networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.