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. We propose a Bayesian Steerable-CNN that places posterior distributions over the basis coefficients, yielding stochastic kernels while preserving equivariance exactly. The loss function of the model is obtained via variational inference and m
The increasing sophistication of AI models and the critical need for reliable, interpretable AI in real-world applications are driving the convergence of robust architectural design with uncertainty quantification.
This development addresses a key limitation in current AI systems by enabling both geometric consistency and trustworthy confidence estimates, which are vital for deployment in sensitive or high-stakes environments.
AI models can now maintain spatial equivariance while inherently providing uncertainty measures, reducing the risk of blind spots in critical decision-making and broadening their applicability.
- · AI Safety Researchers
- · Robotics Developers
- · Medical Imaging
- · Autonomous Systems
- · Deterministic AI Models in Safety-Critical Applications
- · Sectors reliant on purely black-box AI
Integrates uncertainty quantification into geometrically robust neural network architectures, enhancing reliability.
Accelerates deployment of AI in fields requiring high-assurance predictions, such as autonomous vehicles and precision medicine.
Could lead to more transparent and auditable AI systems, fostering greater public trust and accelerating AI regulation debates.
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Read at arXiv cs.AI