Uncertainty Quantification Comparison Hub
4 papers - avg viability 4.3
Top Papers
- Softmax is not Enough (for Adaptive Conformal Classification)(5.0)
Enhancing conformal classification adaptiveness using Helmholtz Free Energy for better uncertainty quantification.
- Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks(5.0)
Improve neural network uncertainty quantification using quantum-inspired complex-valued unitary representations.
- Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study(5.0)
Develop an advanced toolkit for fine-grained uncertainty quantification in long-form language model outputs.
- Cross-Domain Uncertainty Quantification for Selective Prediction: A Comprehensive Bound Ablation with Transfer-Informed Betting(4.0)
A novel approach to selective prediction using Transfer-Informed Betting for improved risk control in data-scarce environments.
- Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers(4.0)
VMoER provides a scalable Bayesian framework for calibrated uncertainty in Mixture-of-Experts Transformers.
- On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference(2.0)
Unify RND with deep ensembles and Bayesian inference for uncertainty quantification.
- MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems(2.0)
A novel approach to uncertainty quantification in dynamical systems using MCMC-informed neural emulators.