#evidential-learning News & Analysis

3 articles tagged with #evidential-learning. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Apr 137/10

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Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation

Researchers propose Evidential Transformation Network (ETN), a lightweight post-hoc module that converts pretrained models into evidential models for uncertainty estimation without retraining. ETN operates in logit space using sample-dependent affine transformations and Dirichlet distributions, demonstrating improved uncertainty quantification across vision and language benchmarks with minimal computational overhead.

AIBullisharXiv – CS AI · Mar 57/10

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Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning

Researchers developed Conflict-aware Evidential Deep Learning (C-EDL), a new uncertainty quantification approach that significantly improves AI model reliability against adversarial attacks and out-of-distribution data. The method achieves up to 90% reduction in adversarial data coverage and 55% reduction in out-of-distribution data coverage without requiring model retraining.

AINeutralarXiv – CS AI · Apr 64/10

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Equivariant Evidential Deep Learning for Interatomic Potentials

Researchers developed e²IP, a new framework for uncertainty quantification in machine learning interatomic potentials used in molecular dynamics simulations. The method uses equivariant evidential deep learning to model atomic forces and their uncertainty through symmetric covariance tensors that transform properly under rotations.

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