{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:RIORYRLPTXFZX3QDJFWCKU5MUO","short_pith_number":"pith:RIORYRLP","schema_version":"1.0","canonical_sha256":"8a1d1c456f9dcb9bee03496c2553aca382e94c6a6cc1fd106b9cf41d3e8424f2","source":{"kind":"arxiv","id":"2006.09661","version":1},"attestation_state":"computed","paper":{"title":"Implicit Neural Representations with Periodic Activation Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Alexander W. Bergman, David B. Lindell, Gordon Wetzstein, Julien N. P. Martel, Vincent Sitzmann","submitted_at":"2020-06-17T05:13:33Z","abstract_excerpt":"Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for "},"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":"2006.09661","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-17T05:13:33Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"f38ded95c8ea8076665a6bb860e6ca3c8128b4d40bdb7535341d51ffe1f24184","abstract_canon_sha256":"a16fefb247b90604fc04d0c35936b93c2c62c8068dc7f6ee6b7467dc0fa04251"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:03.947409Z","signature_b64":"Xm2wfeGuiJBj8tfcDrnLKbx4trQhHFPyJOiAKnOzjcr6dPXrYC/yTbB6aKo6LIFBuQnEcXYLVaGaYgWdd3gKDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a1d1c456f9dcb9bee03496c2553aca382e94c6a6cc1fd106b9cf41d3e8424f2","last_reissued_at":"2026-07-05T01:11:03.946975Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:03.946975Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Implicit Neural Representations with Periodic Activation Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Alexander W. Bergman, David B. Lindell, Gordon Wetzstein, Julien N. P. Martel, Vincent Sitzmann","submitted_at":"2020-06-17T05:13:33Z","abstract_excerpt":"Implicitly defined, continuous, differentiable signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional representations. However, current network architectures for such implicit neural representations are incapable of modeling signals with fine detail, and fail to represent a signal's spatial and temporal derivatives, despite the fact that these are essential to many physical signals defined implicitly as the solution to partial differential equations. We propose to leverage periodic activation functions for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.09661","kind":"arxiv","version":1},"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/2006.09661/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":"2006.09661","created_at":"2026-07-05T01:11:03.947039+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.09661v1","created_at":"2026-07-05T01:11:03.947039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.09661","created_at":"2026-07-05T01:11:03.947039+00:00"},{"alias_kind":"pith_short_12","alias_value":"RIORYRLPTXFZ","created_at":"2026-07-05T01:11:03.947039+00:00"},{"alias_kind":"pith_short_16","alias_value":"RIORYRLPTXFZX3QD","created_at":"2026-07-05T01:11:03.947039+00:00"},{"alias_kind":"pith_short_8","alias_value":"RIORYRLP","created_at":"2026-07-05T01:11:03.947039+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.24876","citing_title":"IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25598","citing_title":"SurfSurg6D: Geometry Consistent Dense Correspondence for Textureless Surgical Instrument Pose Estimation","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2410.01990","citing_title":"Deep Learning Alternatives of the Kolmogorov Superposition Theorem","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22862","citing_title":"Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03463","citing_title":"First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06298","citing_title":"Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22862","citing_title":"Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06298","citing_title":"Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2010.08895","citing_title":"Fourier Neural Operator for Parametric Partial Differential Equations","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16449","citing_title":"Gaussian Field Representations for Turbulent Flow: Compression, Scale Separation, and Physical Fidelity","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08329","citing_title":"DiV-INR: Extreme Low-Bitrate Diffusion Video Compression with INR Conditioning","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO","json":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO.json","graph_json":"https://pith.science/api/pith-number/RIORYRLPTXFZX3QDJFWCKU5MUO/graph.json","events_json":"https://pith.science/api/pith-number/RIORYRLPTXFZX3QDJFWCKU5MUO/events.json","paper":"https://pith.science/paper/RIORYRLP"},"agent_actions":{"view_html":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO","download_json":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO.json","view_paper":"https://pith.science/paper/RIORYRLP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.09661&json=true","fetch_graph":"https://pith.science/api/pith-number/RIORYRLPTXFZX3QDJFWCKU5MUO/graph.json","fetch_events":"https://pith.science/api/pith-number/RIORYRLPTXFZX3QDJFWCKU5MUO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO/action/storage_attestation","attest_author":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO/action/author_attestation","sign_citation":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO/action/citation_signature","submit_replication":"https://pith.science/pith/RIORYRLPTXFZX3QDJFWCKU5MUO/action/replication_record"}},"created_at":"2026-07-05T01:11:03.947039+00:00","updated_at":"2026-07-05T01:11:03.947039+00:00"}