Uncertainty - HanjieChen/Reading-List GitHub Wiki
- Benchmarking LLMs via Uncertainty Quantification
- GETTING A CLUE: A METHOD FOR EXPLAINING UNCERTAINTY ESTIMATES?
- BAYES-TREX: a Bayesian Sampling Approach to Model Transparency by Example
- Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers
- Uncertainty Interpretation of the Machine Learning Survival Model Predictions
- To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
- On the Calibration and Uncertainty of Neural Learning to Rank Models
- Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
- Uncertainty-Aware Curriculum Learning for Neural Machine Translation
- Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
- Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
- Evaluating Predictive Uncertainty under Distributional Shift on Dialogue Dataset
- Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates
- Uncertainty Baselines:Benchmarks for Uncertainty & Robustness in Deep Learning
- Uncertainty-Aware Label Refinement for Sequence Labeling
- IMPROVED UNCERTAINTY POST-CALIBRATION VIA RANK PRESERVING TRANSFORMS
- GRADIENTS AS A MEASURE OF UNCERTAINTY IN NEURAL NETWORKS
- On Calibration of Modern Neural Networks
- Accurate Uncertainties for Deep Learning Using Calibrated Regression
- Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings
- Addressing Failure Prediction by Learning Model Confidence
- Uncertainty-Aware Attention for Reliable Interpretation and Prediction
- Uncertainty-aware Self-training for Few-shot Text Classification
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
- The need for uncertainty quantification in machine-assisted medical decision making
- Using Pre-Training Can Improve Model Robustness and Uncertainty
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
- Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
- Calibrate Before Use Improving Few-Shot Performance of Language Models
- To Trust Or Not To Trust A Classifie
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
- On Calibration of Modern Neural Networks
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
- Accurate Layerwise Interpretable Competence Estimation
- EXPLAINING IMAGE CLASSIFIERS BY COUNTERFACTUAL GENERATION
- A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
- Calibration of Pre-trained Transformers
- A BASELINE FOR DETECTING MISCLASSIFIED AND OUT-OF-DISTRIBUTION EXAMPLES IN NEURAL NETWORKS
- Pathologies of Neural Models Make Interpretations Difficult
- Explaining by Removing: A Unified Framework for Model Explanation
- Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
- Can Explanations Be Useful for Calibrating Black Box Models?
- Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
- Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates
- Effects of Uncertainty on the Quality of Feature Importance Explanations
- Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
- Path Integrals for the Attribution of Model Uncertainties
- Contrastive Explanations for Model Interpretability
- Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
- Attribution of Predictive Uncertainties in Classification Models
- Reliable Post hoc Explanations: Modeling Uncertainty in Explainability