{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2B6NKJHHX7ZSP55AEKQC4CIZWY","short_pith_number":"pith:2B6NKJHH","schema_version":"1.0","canonical_sha256":"d07cd524e7bff327f7a022a02e0919b62690bb02bb4ebc375eb561d8665d8e92","source":{"kind":"arxiv","id":"2502.07746","version":2},"attestation_state":"computed","paper":{"title":"HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.AT"],"primary_cat":"cs.LG","authors_text":"Christopher Tape, David R Johnson, Dhananjay Bhaskar, Hiren Madhu, Ian Adelstein, Jake Kovalic, Michael Perlmutter, Rex Ying, Siddharth Viswanath, Smita Krishnaswamy","submitted_at":"2025-02-11T18:13:29Z","abstract_excerpt":"In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our work is motivated by single-cell data which can have very high-dimensionality --exceeding the capabilities of existing methods for point clouds which are mostly tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e., one data set for every patient), necessitating models that can process large, high-dimensional point-clouds at scale. Most current approaches b"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.07746","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-11T18:13:29Z","cross_cats_sorted":["math.AT"],"title_canon_sha256":"af85dd08742497c6399cdde94e51d208ac5994a7771645d73c9409b591ee6787","abstract_canon_sha256":"0c139cbb5795eda8726a5b2486302e663005e08004f300cd3cf6d3ff6336b86e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:04.797207Z","signature_b64":"6aE4LiJ3Zrb9hFkhKRX6arODbiKDUrLYSlR8Nic1l9Z+5vbVQ6ODkCULxnXlJ2GH6a94uUTYBKW7b6ZgGaG3AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d07cd524e7bff327f7a022a02e0919b62690bb02bb4ebc375eb561d8665d8e92","last_reissued_at":"2026-07-05T11:10:04.796677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:04.796677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.AT"],"primary_cat":"cs.LG","authors_text":"Christopher Tape, David R Johnson, Dhananjay Bhaskar, Hiren Madhu, Ian Adelstein, Jake Kovalic, Michael Perlmutter, Rex Ying, Siddharth Viswanath, Smita Krishnaswamy","submitted_at":"2025-02-11T18:13:29Z","abstract_excerpt":"In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Our work is motivated by single-cell data which can have very high-dimensionality --exceeding the capabilities of existing methods for point clouds which are mostly tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e., one data set for every patient), necessitating models that can process large, high-dimensional point-clouds at scale. Most current approaches b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.07746","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.07746/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.07746","created_at":"2026-07-05T11:10:04.796739+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.07746v2","created_at":"2026-07-05T11:10:04.796739+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.07746","created_at":"2026-07-05T11:10:04.796739+00:00"},{"alias_kind":"pith_short_12","alias_value":"2B6NKJHHX7ZS","created_at":"2026-07-05T11:10:04.796739+00:00"},{"alias_kind":"pith_short_16","alias_value":"2B6NKJHHX7ZSP55A","created_at":"2026-07-05T11:10:04.796739+00:00"},{"alias_kind":"pith_short_8","alias_value":"2B6NKJHH","created_at":"2026-07-05T11:10:04.796739+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15524","citing_title":"Neural Point-Forms","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY","json":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY.json","graph_json":"https://pith.science/api/pith-number/2B6NKJHHX7ZSP55AEKQC4CIZWY/graph.json","events_json":"https://pith.science/api/pith-number/2B6NKJHHX7ZSP55AEKQC4CIZWY/events.json","paper":"https://pith.science/paper/2B6NKJHH"},"agent_actions":{"view_html":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY","download_json":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY.json","view_paper":"https://pith.science/paper/2B6NKJHH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.07746&json=true","fetch_graph":"https://pith.science/api/pith-number/2B6NKJHHX7ZSP55AEKQC4CIZWY/graph.json","fetch_events":"https://pith.science/api/pith-number/2B6NKJHHX7ZSP55AEKQC4CIZWY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY/action/storage_attestation","attest_author":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY/action/author_attestation","sign_citation":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY/action/citation_signature","submit_replication":"https://pith.science/pith/2B6NKJHHX7ZSP55AEKQC4CIZWY/action/replication_record"}},"created_at":"2026-07-05T11:10:04.796739+00:00","updated_at":"2026-07-05T11:10:04.796739+00:00"}