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Uncertainty Quantification
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Sensor Response-Time Reduction using Long-Short Term Memory Network Forecasting
Statistical Machine Learning for Astronomy -- A Textbook
Distributional Reinforcement Learning on Path-dependent Options
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Joint space-time wind field data extrapolation and uncertainty quantification using nonparametric Bayesian dictionary learning
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion
Uncertainty Quantification for Motor Imagery BCI -- Machine Learning vs. Deep Learning
Estimating prevalence with precision and accuracy
UQLM: A Python Package for Uncertainty Quantification in Large Language Models
NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models
Deterministic Object Pose Confidence Region Estimation
Forecasting Geopolitical Events with a Sparse Temporal Fusion Transformer and Gaussian Process Hybrid: A Case Study in Middle Eastern and U.S. Conflict Dynamics
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees
Latent-space Field Tension for Astrophysical Component Detection An application to X-ray imaging
Structural System Identification via Validation and Adaptation
Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
When Can We Reuse a Calibration Set for Multiple Conformal Predictions?
A Framework for Uncertainty Quantification Based on Nearest Neighbors Across Layers
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
GNN's Uncertainty Quantification using Self-Distillation
Learning Personalized Utility Functions for Drivers in Ride-hailing Systems Using Ensemble Hypernetworks
Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis
Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction
UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making
Semantic and Feature Guided Uncertainty Quantification of Visual Localization for Autonomous Vehicles
Vine Copulas as Differentiable Computational Graphs
Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localizations for better covariance estimation
Bridging Data-Driven and Physics-Based Models: A Consensus Multi-Model Kalman Filter for Robust Vehicle State Estimation
Model-Agnostic, Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning
Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification
A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions
Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation
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