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Found 8 papers, 6 papers with code
Differentiable and accelerated wavelet transforms on the sphere and ball
We observe up to a $300$-fold and $21800$-fold acceleration for signals on the sphere and ball, respectively, compared to existing software, whilst maintaining 64-bit machine precision.
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings.
Differentiable and accelerated spherical harmonic and Wigner transforms
We develop novel algorithmic structures for accelerated and differentiable computation of generalised Fourier transforms on the sphere $\mathbb{S}^2$ and rotation group $\text{SO}(3)$, i. e. spherical harmonic and Wigner transforms, respectively.
Proximal nested sampling with data-driven priors for physical scientists
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.
Learned harmonic mean estimation of the marginal likelihood with normalizing flows
The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood.
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
For 4k spherical images we realize a saving of $10^9$ in computational cost and $10^4$ in memory usage when compared to the most efficient alternative equivariant spherical convolution.
Efficient Generalized Spherical CNNs
We develop two new strictly equivariant layers with reduced complexity $\mathcal{O}(CL^4)$ and $\mathcal{O}(CL^3 \log L)$, making larger, more expressive models computationally feasible.
Scale-discretised ridgelet transform on the sphere
The restriction to antipodal signals is expected since the spherical Radon and ridgelet transforms themselves result in signals that exhibit antipodal symmetry.
Information Theory Information Theory
