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Found 5 papers, 3 papers with code
Benchmarking Predictive Coding Networks -- Made Simple
In this work, we tackle the problems of efficiency and scalability for predictive coding networks in machine learning.
Towards Certification of Uncertainty Calibration under Adversarial Attacks
We show that attacks can significantly harm calibration, and thus propose certified calibration as worst-case bounds on calibration under adversarial perturbations.
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience.
Explaining Chest X-ray Pathologies in Natural Language
Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice.
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
e-ViL is a benchmark for explainable vision-language tasks that establishes a unified evaluation framework and provides the first comprehensive comparison of existing approaches that generate NLEs for VL tasks.
