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      "statement": "# LieConv continuous-data group convolutions\n\nLifts continuous coordinates into a Lie group and parameterizes convolution kernels through its Lie algebra, enabling local learned convolutions on irregularly sampled data.\n\n## Scientific scope\n\nOriginal construction restricts to groups with surjective exponential maps and appropriate lifting/sampling. Monte Carlo convolution is equivariant in distribution, not every finite random realization exactly equivariant. No unrestricted all-Lie-group or integrator-level energy conservation claim; inherits group convolution and efficient PointConv.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMarc Finzi; Samuel Stanton; Pavel Izmailov; Andrew Gordon Wilson\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data](https://arxiv.org/pdf/2002.12880v1)\n",
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