Search Results for author: Matthew A. Price

Found 8 papers, 6 papers with code

Differentiable and accelerated wavelet transforms on the sphere and ball

1 code implementation2 Feb 2024 Matthew A. Price, Alicja Polanska, Jessica Whitney, Jason D. McEwen

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

1 code implementation30 Nov 2023 Tobías I. Liaudat, Matthijs Mars, Matthew A. Price, Marcelo Pereyra, Marta M. Betcke, Jason D. McEwen

This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings.

Uncertainty Quantification

Differentiable and accelerated spherical harmonic and Wigner transforms

1 code implementation24 Nov 2023 Matthew A. Price, Jason D. McEwen

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

1 code implementation30 Jun 2023 Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai, Marcelo Pereyra

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.

Model Selection

Learned harmonic mean estimation of the marginal likelihood with normalizing flows

1 code implementation30 Jun 2023 Alicja Polanska, Matthew A. Price, Alessio Spurio Mancini, Jason D. McEwen

The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood.

Model Selection

Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions

no code implementations27 Sep 2022 Jeremy Ocampo, Matthew A. Price, Jason D. McEwen

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.

Depth Estimation Semantic Segmentation

Efficient Generalized Spherical CNNs

1 code implementation ICLR 2021 Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price, Mayeul d'Avezac, Jason D. McEwen

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

no code implementations6 Oct 2015 Jason D. McEwen, Matthew A. Price

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

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