model calibration

Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit ICLR 2021 Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models EACL 2021 Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing ICLR 2021 Learning Neural Network Subspaces ICML 2021 Learning with Retrospection AAAI 2021 Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances AAAI 2021 Mitigating Political Bias in Language Models through Reinforced Calibration AAAI 2021 On Calibration and Out-of-Domain Generalization NeurIPS 2021 On Model Calibration for Long-Tailed Object Detection and Instance Segmentation NeurIPS 2021 On the Effects of Transformer Size on In- and Out-of-Domain Calibration EMNLP 2021 Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence NeurIPS 2021 Revisiting the Calibration of Modern Neural Networks NeurIPS 2021 Sample efficient learning of image-based diagnostic classifiers via probabilistic labels AISTATS 2021 Searching for Search Errors in Neural Morphological Inflection EACL 2021 Simple and Effective Stochastic Neural Networks AAAI 2021 Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective NeurIPS 2021 Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration AAAI 2021 Uncertainty Calibration for Ensemble-Based Debiasing Methods NeurIPS 2021 Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs ICLR 2021 Uplift Modeling with High Class Imbalance ACML 2021 Angular Visual Hardness ICML 2020 Being Bayesian about Categorical Probability ICML 2020 Calibration of Pre-trained Transformers EMNLP 2020 Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach EMNLP 2020 Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast—Choose Three EMNLP 2020