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Affiliation:, University College London, King's College London
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Physical symmetries are no longer a training burden: this SO(3)-equivariant CNN on the sphere achieves sub-percent invariance error without learning, unlocking more efficient and accurate scientific discovery.
Absolute model evidence, a cornerstone of Bayesian inference, is now accurately and amortized estimable for complex models, unlocking robust model misspecification tests.
Achieving accurate cosmological inference with just 60 high-fidelity simulations could revolutionize the cost-effectiveness of weak lensing studies.
Identifying and resolving parameter degeneracies can cut simulation costs by up to 10x while enhancing our understanding of complex models.