arXiv AI June 16, 2026 By Abhishek Keripale, Ponkrshnan Thiagarajan, Susanta Ghosh

Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously

arXiv:2606. 15479v1 Announce Type: cross Abstract: Steerable convolutional neural networks (Steerable-CNNs) guarantee SE(3)-equivariance by parameterizing kernels as linear combinations of steerable basis functions, but their deterministic nature precludes uncertainty quantification - limiting their use in settings where confidence estimates are essential.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv Machine Learning

Jul 31

Uncertainty quantification for trustworthy deep learning: Methods and measures

arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.

By H. Martin Gillis, Thomas Trappenberg

llms safety

arXiv Machine Learning

Jun 15

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.

By Luis A. Ortega

arXiv Machine Learning

Aug 7

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

arXiv:2608. 05995v1 Announce Type: new Abstract: Reliable uncertainty estimates are critical in safety-sensitive applications, where understanding the sources of predictive uncertainty is essential.

By Frieder Wizgall, Georg Tirpitz, Moritz Seiler, Kerstin Ritter, B\'alint Mucs\'anyi

benchmarks safety

arXiv AI

Jun 16

Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning

arXiv:2606. 16214v1 Announce Type: cross Abstract: Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications.

By Tobias Jan Wieczorek, Leon de Andrade, Thomas M\"ollenhoff, Marcus Rohrbach

llms

arXiv AI

Jul 29

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

arXiv:2607. 25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function.

By Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch, Markus Goetz, Achim Streit, Sebastian Krumscheid, Charlotte Debus

arXiv Machine Learning

Jun 2

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning

arXiv:2412. 04177v2 Announce Type: replace Abstract: Recently, there has been an increasing interest in performing post-hoc uncertainty estimation about the predictions of pre-trained deep neural networks (DNNs).

By Luis A. Ortega, Sim\'on Rodr\'iguez-Santana, Daniel Hern\'andez-Lobato

benchmarks

Related stories

Uncertainty quantification for trustworthy deep learning: Methods and measures

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

A Unified Risk View of Uncertainty: Posterior Risk for Disentanglement and Evaluation Beyond Proxies

Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning