activity
20242026
most citedTowards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2026

Protection Levels for Vision-Based Pose Estimation

Olivia Beyer Bruvik, Romeo Valentin, Marc R. Schlichting +2

Vision-based navigation complements Global Navigation Satellite Systems, but certification demands integrity guarantees that account for faulty measurements. Previous work presente…

cs.LG2026

Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System

Romeo Valentin, Olivia Beyer Bruvik, Marc R. Schlichting +1

EASA's learning-assurance guidance requires data-driven aviation systems to build and monitor their own situation representation, yet for neural networks the technical means to pro…

stat.ME2026

Recipes for Calibration Checks in Safety-Critical Applications

Romeo Valentin

Safety-critical prediction systems, such as autonomous vehicles, weather forecasters, and medical monitors, commonly rely on probabilistic forecasters. These forecasters make predi…

cs.CV2025

Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System

Romeo Valentin, Sydney M. Katz, Artur B. Carneiro +2

Recent advances in data-driven computer vision have enabled robust autonomous navigation capabilities for civil aviation, including automated landing and runway detection. However,…

cs.LG2025

DB-KSVD: Scalable Alternating Optimization for Disentangling High-Dimensional Embedding Spaces

Romeo Valentin, Sydney M. Katz, Vincent Vanhoucke +1

Dictionary learning has recently emerged as a promising approach for mechanistic interpretability of large transformer models. Disentangling high-dimensional transformer embeddings…

cs.RO2024

Probabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features

Romeo Valentin, Sydney M. Katz, Joonghyun Lee +3

This paper addresses the challenge of probabilistic parameter estimation given measurement uncertainty in real-time. We provide a general formulation and apply this to pose estimat…

cs.LG2024★ 1 cited

Towards a Framework for Deep Learning Certification in Safety-Critical Applications Using Inherently Safe Design and Run-Time Error Detection

Romeo Valentin

Although an ever-growing number of applications employ deep learning based systems for prediction, decision-making, or state estimation, almost no certification processes have been…