keypoint detection 1pose estimation 1probabilistic calibration 1sensor fusion 1uncertainty quantification 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CV2026
From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models
Alexej Klushyn, Juan Rivero Sesma, Florian Seligmann +3
The paper adds a lightweight post‑hoc module to trained YOLO‑Pose models that predicts calibrated predictive distributions for each keypoint, enabling uncertainty‑aware ranking and…
cs.LG2026
Latent Matters: Learning Deep State-Space Models
Alexej Klushyn, Richard Kurle, Maximilian Soelch +2
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower b…
cs.LG2024
BALI: Learning Neural Networks via Bayesian Layerwise Inference
Richard Kurle, Alexej Klushyn, Ralf Herbrich
We introduce a new method for learning Bayesian neural networks, treating them as a stack of multivariate Bayesian linear regression models. The main idea is to infer the layerwise…