{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QYNKER35FOIGSC3QMEAWAHTD3L","short_pith_number":"pith:QYNKER35","schema_version":"1.0","canonical_sha256":"861aa2477d2b90690b706101601e63dafaafed164c130e603031aaee35d7b1e6","source":{"kind":"arxiv","id":"1802.10399","version":3},"attestation_state":"computed","paper":{"title":"Compressing Neural Networks using the Variational Information Bottleneck","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Dai, Chen Zhu, David Wipf","submitted_at":"2018-02-28T13:26:46Z","abstract_excerpt":"Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we focus on pruning individual neurons, which can simultaneously trim model size, FLOPs, and run-time memory. To improve upon the performance of existing compression algorithms we utilize the information bottleneck principle instantiated via a tractable variational bound. Minimization of this information theoretic bound reduces the redundancy between adjacent layers by aggregating useful information into a subset of neur"},"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":"1802.10399","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-28T13:26:46Z","cross_cats_sorted":[],"title_canon_sha256":"54eb84b6d415733ae8bc713b177a3ce3ddf60c7d50f3bafe19625871a7ec6936","abstract_canon_sha256":"a8755448865e9a415170a67e454e5cc6fb59696f75305388bb81a7dd5bd14db1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:03.734990Z","signature_b64":"EzFjaehjSroET7VmvvM7/ADqphOlLIHFjql2BoKMJId0iwx0WACO5SNCEmiZf+9smIln4BlxP1A+VS3J+y1VAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"861aa2477d2b90690b706101601e63dafaafed164c130e603031aaee35d7b1e6","last_reissued_at":"2026-05-18T00:18:03.734365Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:03.734365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compressing Neural Networks using the Variational Information Bottleneck","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Dai, Chen Zhu, David Wipf","submitted_at":"2018-02-28T13:26:46Z","abstract_excerpt":"Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we focus on pruning individual neurons, which can simultaneously trim model size, FLOPs, and run-time memory. To improve upon the performance of existing compression algorithms we utilize the information bottleneck principle instantiated via a tractable variational bound. Minimization of this information theoretic bound reduces the redundancy between adjacent layers by aggregating useful information into a subset of neur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.10399","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":""},"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":"1802.10399","created_at":"2026-05-18T00:18:03.734454+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.10399v3","created_at":"2026-05-18T00:18:03.734454+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.10399","created_at":"2026-05-18T00:18:03.734454+00:00"},{"alias_kind":"pith_short_12","alias_value":"QYNKER35FOIG","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"QYNKER35FOIGSC3Q","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"QYNKER35","created_at":"2026-05-18T12:32:50.500415+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/QYNKER35FOIGSC3QMEAWAHTD3L","json":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L.json","graph_json":"https://pith.science/api/pith-number/QYNKER35FOIGSC3QMEAWAHTD3L/graph.json","events_json":"https://pith.science/api/pith-number/QYNKER35FOIGSC3QMEAWAHTD3L/events.json","paper":"https://pith.science/paper/QYNKER35"},"agent_actions":{"view_html":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L","download_json":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L.json","view_paper":"https://pith.science/paper/QYNKER35","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.10399&json=true","fetch_graph":"https://pith.science/api/pith-number/QYNKER35FOIGSC3QMEAWAHTD3L/graph.json","fetch_events":"https://pith.science/api/pith-number/QYNKER35FOIGSC3QMEAWAHTD3L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L/action/storage_attestation","attest_author":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L/action/author_attestation","sign_citation":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L/action/citation_signature","submit_replication":"https://pith.science/pith/QYNKER35FOIGSC3QMEAWAHTD3L/action/replication_record"}},"created_at":"2026-05-18T00:18:03.734454+00:00","updated_at":"2026-05-18T00:18:03.734454+00:00"}