{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XRFGYQNEY37BZKVFHVQPJGMYH4","short_pith_number":"pith:XRFGYQNE","schema_version":"1.0","canonical_sha256":"bc4a6c41a4c6fe1caaa53d60f499983f33edaa9231e11367823d3ecd32066752","source":{"kind":"arxiv","id":"2105.11166","version":6},"attestation_state":"computed","paper":{"title":"AirNet: Neural Network Transmission over the Air","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NI","authors_text":"Deniz Gunduz, Krystian Mikolajczyk, Mikolaj Jankowski","submitted_at":"2021-05-24T09:16:04Z","abstract_excerpt":"State-of-the-art performance for many edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location- and time-sensitive, and must be delivered over a wireless channel rapidly and efficiently. In this paper, we introduce AirNet, a family of novel training and transmission methods that allow DNNs to be efficiently delivered over wireless channels under stringent transmit power and latency constraints. This corresponds to a new class of joint source-channel coding problems, aimed at delivering DNNs with the goal of maximizing their accuracy at the receiver, rather t"},"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":"2105.11166","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2021-05-24T09:16:04Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d2481db65a7b585836d7286d5bb9600ab1f1c8a8846f08da974d80a117aa6b53","abstract_canon_sha256":"d05b370817b4613a91a8b6a9ea5f211d0bb91ed7770c0c53b60749ce71414dde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:32:51.789581Z","signature_b64":"oZUGwjAmwN2pV0Y2OHRjD+Y+CK+MVbziGfpO6ArsZsfm9++lVho7sCY7j3T6Km5vNoXeSK772meSptW2hld0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc4a6c41a4c6fe1caaa53d60f499983f33edaa9231e11367823d3ecd32066752","last_reissued_at":"2026-07-05T06:32:51.789180Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:32:51.789180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AirNet: Neural Network Transmission over the Air","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.NI","authors_text":"Deniz Gunduz, Krystian Mikolajczyk, Mikolaj Jankowski","submitted_at":"2021-05-24T09:16:04Z","abstract_excerpt":"State-of-the-art performance for many edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location- and time-sensitive, and must be delivered over a wireless channel rapidly and efficiently. In this paper, we introduce AirNet, a family of novel training and transmission methods that allow DNNs to be efficiently delivered over wireless channels under stringent transmit power and latency constraints. This corresponds to a new class of joint source-channel coding problems, aimed at delivering DNNs with the goal of maximizing their accuracy at the receiver, rather t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.11166","kind":"arxiv","version":6},"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/2105.11166/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":"2105.11166","created_at":"2026-07-05T06:32:51.789234+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.11166v6","created_at":"2026-07-05T06:32:51.789234+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.11166","created_at":"2026-07-05T06:32:51.789234+00:00"},{"alias_kind":"pith_short_12","alias_value":"XRFGYQNEY37B","created_at":"2026-07-05T06:32:51.789234+00:00"},{"alias_kind":"pith_short_16","alias_value":"XRFGYQNEY37BZKVF","created_at":"2026-07-05T06:32:51.789234+00:00"},{"alias_kind":"pith_short_8","alias_value":"XRFGYQNE","created_at":"2026-07-05T06:32:51.789234+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.10070","citing_title":"Topological Neural Networks over the Air","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4","json":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4.json","graph_json":"https://pith.science/api/pith-number/XRFGYQNEY37BZKVFHVQPJGMYH4/graph.json","events_json":"https://pith.science/api/pith-number/XRFGYQNEY37BZKVFHVQPJGMYH4/events.json","paper":"https://pith.science/paper/XRFGYQNE"},"agent_actions":{"view_html":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4","download_json":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4.json","view_paper":"https://pith.science/paper/XRFGYQNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.11166&json=true","fetch_graph":"https://pith.science/api/pith-number/XRFGYQNEY37BZKVFHVQPJGMYH4/graph.json","fetch_events":"https://pith.science/api/pith-number/XRFGYQNEY37BZKVFHVQPJGMYH4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4/action/storage_attestation","attest_author":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4/action/author_attestation","sign_citation":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4/action/citation_signature","submit_replication":"https://pith.science/pith/XRFGYQNEY37BZKVFHVQPJGMYH4/action/replication_record"}},"created_at":"2026-07-05T06:32:51.789234+00:00","updated_at":"2026-07-05T06:32:51.789234+00:00"}