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These SE(3)-equivariant convolutions utilize kernels which are\nparameterized as a linear combination of a complete steerable kernel basis,\nwhich is derived analytically in this paper. We prove that equivariant\nconvolutions are the most general equivariant linear maps between fields over\nR^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs\nfor the problem of amino acid propensity prediction and protein structure\nclassification, both of which have inherent SE(3) symmetry.","url_abs":"http://arxiv.org/abs/1807.02547v2","url_pdf":"http://arxiv.org/pdf/1807.02547v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"3d-steerable-cnns-learning-rotationally","repo_url":"https://github.com/mariogeiger/se3cnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"3d-steerable-cnns-learning-rotationally","repo_url":"https://github.com/e3nn/e3nn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"3d-steerable-cnns-learning-rotationally","repo_url":"https://github.com/e3nn/e3nn-jax","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.02547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.02547"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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