{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YHYAL3QWF2DANCGDPIMQSME6BB","short_pith_number":"pith:YHYAL3QW","schema_version":"1.0","canonical_sha256":"c1f005ee162e860688c37a1909309e085e80cdd9869e827742515f372ea4fb07","source":{"kind":"arxiv","id":"2301.09479","version":3},"attestation_state":"computed","paper":{"title":"Modality-Agnostic Variational Compression of Implicit Neural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Jaeho Lee, Jihoon Tack, Jinwoo Shin, Jonathan Richard Schwarz, Yee Whye Teh","submitted_at":"2023-01-23T15:22:42Z","abstract_excerpt":"We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. This allows the specialisation of a shared INR network to each data item through subnetwork selection. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression. Variational Compression of Impli"},"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":"2301.09479","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-01-23T15:22:42Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"7b6fa97be90039b8359d9e06db31759130df8ef71124af6f302b20dc51bc31b0","abstract_canon_sha256":"34b304cf14b2784bb446d97cbf8d834bb4d3cf647c1d5da31f8f1450224d5504"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:58:53.231709Z","signature_b64":"xjvcrv+V9M+R7huSoFNC5Vlu1ay9lkGXEADioSHq0r6HTHvCVlHkAzCyHFbkFoYBMqgvLPHx8tRyWOrwDy8ZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1f005ee162e860688c37a1909309e085e80cdd9869e827742515f372ea4fb07","last_reissued_at":"2026-07-05T05:58:53.231278Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:58:53.231278Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modality-Agnostic Variational Compression of Implicit Neural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Jaeho Lee, Jihoon Tack, Jinwoo Shin, Jonathan Richard Schwarz, Yee Whye Teh","submitted_at":"2023-01-23T15:22:42Z","abstract_excerpt":"We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. This allows the specialisation of a shared INR network to each data item through subnetwork selection. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression. Variational Compression of Impli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09479","kind":"arxiv","version":3},"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/2301.09479/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":"2301.09479","created_at":"2026-07-05T05:58:53.231344+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.09479v3","created_at":"2026-07-05T05:58:53.231344+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09479","created_at":"2026-07-05T05:58:53.231344+00:00"},{"alias_kind":"pith_short_12","alias_value":"YHYAL3QWF2DA","created_at":"2026-07-05T05:58:53.231344+00:00"},{"alias_kind":"pith_short_16","alias_value":"YHYAL3QWF2DANCGD","created_at":"2026-07-05T05:58:53.231344+00:00"},{"alias_kind":"pith_short_8","alias_value":"YHYAL3QW","created_at":"2026-07-05T05:58:53.231344+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB","json":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB.json","graph_json":"https://pith.science/api/pith-number/YHYAL3QWF2DANCGDPIMQSME6BB/graph.json","events_json":"https://pith.science/api/pith-number/YHYAL3QWF2DANCGDPIMQSME6BB/events.json","paper":"https://pith.science/paper/YHYAL3QW"},"agent_actions":{"view_html":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB","download_json":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB.json","view_paper":"https://pith.science/paper/YHYAL3QW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.09479&json=true","fetch_graph":"https://pith.science/api/pith-number/YHYAL3QWF2DANCGDPIMQSME6BB/graph.json","fetch_events":"https://pith.science/api/pith-number/YHYAL3QWF2DANCGDPIMQSME6BB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB/action/storage_attestation","attest_author":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB/action/author_attestation","sign_citation":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB/action/citation_signature","submit_replication":"https://pith.science/pith/YHYAL3QWF2DANCGDPIMQSME6BB/action/replication_record"}},"created_at":"2026-07-05T05:58:53.231344+00:00","updated_at":"2026-07-05T05:58:53.231344+00:00"}