{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:53NZSF6KJEXA6JQWVNP3VKNGBM","short_pith_number":"pith:53NZSF6K","canonical_record":{"source":{"id":"1907.04632","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-07-10T11:44:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"796374463320287b5f3c5823b2317ddfda4d1a17a6c78583773d376fd9ecdf0c","abstract_canon_sha256":"a0a461642d2437e4dd6c3ea28da232edd7a7a79e91a79e3ab57adc4dc761efcb"},"schema_version":"1.0"},"canonical_sha256":"eedb9917ca492e0f2616ab5fbaa9a60b22fe44ffff47ee256eae60df77155568","source":{"kind":"arxiv","id":"1907.04632","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.04632","created_at":"2026-05-17T23:40:57Z"},{"alias_kind":"arxiv_version","alias_value":"1907.04632v1","created_at":"2026-05-17T23:40:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.04632","created_at":"2026-05-17T23:40:57Z"},{"alias_kind":"pith_short_12","alias_value":"53NZSF6KJEXA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"53NZSF6KJEXA6JQW","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"53NZSF6K","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:53NZSF6KJEXA6JQWVNP3VKNGBM","target":"record","payload":{"canonical_record":{"source":{"id":"1907.04632","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-07-10T11:44:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"796374463320287b5f3c5823b2317ddfda4d1a17a6c78583773d376fd9ecdf0c","abstract_canon_sha256":"a0a461642d2437e4dd6c3ea28da232edd7a7a79e91a79e3ab57adc4dc761efcb"},"schema_version":"1.0"},"canonical_sha256":"eedb9917ca492e0f2616ab5fbaa9a60b22fe44ffff47ee256eae60df77155568","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:57.301716Z","signature_b64":"PqfhWNTNd0JcWCE91wxRiaYQlNGs/mQXH2ZxId7t4ch+fJLdNuOHDca9Pw9xLQpZV2C8i1Nl1kpfrOizfUB2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eedb9917ca492e0f2616ab5fbaa9a60b22fe44ffff47ee256eae60df77155568","last_reissued_at":"2026-05-17T23:40:57.301023Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:57.301023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.04632","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:40:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A5qRWwZRaqg6VHcUTR5X36O31qgbgCIbdU7RCWPUgquhqbNi1qGO5pf3oIvLnrmqkCKXYlpAUHxUSV7xFNuUDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T18:20:51.892539Z"},"content_sha256":"a700712616af269cc0210d0497ca06faf2e89e96a3af32018c9e555793c02d4c","schema_version":"1.0","event_id":"sha256:a700712616af269cc0210d0497ca06faf2e89e96a3af32018c9e555793c02d4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:53NZSF6KJEXA6JQWVNP3VKNGBM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Video Action Recognition Via Neural Architecture Searching","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Guoying Zhao, Wei Peng, Xiaopeng Hong","submitted_at":"2019-07-10T11:44:28Z","abstract_excerpt":"Deep neural networks have achieved great success for video analysis and understanding. However, designing a high-performance neural architecture requires substantial efforts and expertise. In this paper, we make the first attempt to let algorithm automatically design neural networks for video action recognition tasks. Specifically, a spatio-temporal network is developed in a differentiable space modeled by a directed acyclic graph, thus a gradient-based strategy can be performed to search an optimal architecture. Nonetheless, it is computationally expensive, since the computational burden to e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.04632","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:40:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"30UpGlxt3W4zJm7fosZovllvzKGa9PDreMf21K+Ra6Y/kYDxPaJECJqyn36BBBB7Mxnpds0dKE9k8ImtGmOaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T18:20:51.892886Z"},"content_sha256":"03b7f232531f37ad21e64b43d985832ba3546ea603aa81d0c84a0e364d50e089","schema_version":"1.0","event_id":"sha256:03b7f232531f37ad21e64b43d985832ba3546ea603aa81d0c84a0e364d50e089"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/53NZSF6KJEXA6JQWVNP3VKNGBM/bundle.json","state_url":"https://pith.science/pith/53NZSF6KJEXA6JQWVNP3VKNGBM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/53NZSF6KJEXA6JQWVNP3VKNGBM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-10T18:20:51Z","links":{"resolver":"https://pith.science/pith/53NZSF6KJEXA6JQWVNP3VKNGBM","bundle":"https://pith.science/pith/53NZSF6KJEXA6JQWVNP3VKNGBM/bundle.json","state":"https://pith.science/pith/53NZSF6KJEXA6JQWVNP3VKNGBM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/53NZSF6KJEXA6JQWVNP3VKNGBM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:53NZSF6KJEXA6JQWVNP3VKNGBM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a0a461642d2437e4dd6c3ea28da232edd7a7a79e91a79e3ab57adc4dc761efcb","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-07-10T11:44:28Z","title_canon_sha256":"796374463320287b5f3c5823b2317ddfda4d1a17a6c78583773d376fd9ecdf0c"},"schema_version":"1.0","source":{"id":"1907.04632","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.04632","created_at":"2026-05-17T23:40:57Z"},{"alias_kind":"arxiv_version","alias_value":"1907.04632v1","created_at":"2026-05-17T23:40:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.04632","created_at":"2026-05-17T23:40:57Z"},{"alias_kind":"pith_short_12","alias_value":"53NZSF6KJEXA","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"53NZSF6KJEXA6JQW","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"53NZSF6K","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:03b7f232531f37ad21e64b43d985832ba3546ea603aa81d0c84a0e364d50e089","target":"graph","created_at":"2026-05-17T23:40:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Deep neural networks have achieved great success for video analysis and understanding. However, designing a high-performance neural architecture requires substantial efforts and expertise. In this paper, we make the first attempt to let algorithm automatically design neural networks for video action recognition tasks. Specifically, a spatio-temporal network is developed in a differentiable space modeled by a directed acyclic graph, thus a gradient-based strategy can be performed to search an optimal architecture. Nonetheless, it is computationally expensive, since the computational burden to e","authors_text":"Guoying Zhao, Wei Peng, Xiaopeng Hong","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-07-10T11:44:28Z","title":"Video Action Recognition Via Neural Architecture Searching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.04632","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a700712616af269cc0210d0497ca06faf2e89e96a3af32018c9e555793c02d4c","target":"record","created_at":"2026-05-17T23:40:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a0a461642d2437e4dd6c3ea28da232edd7a7a79e91a79e3ab57adc4dc761efcb","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2019-07-10T11:44:28Z","title_canon_sha256":"796374463320287b5f3c5823b2317ddfda4d1a17a6c78583773d376fd9ecdf0c"},"schema_version":"1.0","source":{"id":"1907.04632","kind":"arxiv","version":1}},"canonical_sha256":"eedb9917ca492e0f2616ab5fbaa9a60b22fe44ffff47ee256eae60df77155568","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eedb9917ca492e0f2616ab5fbaa9a60b22fe44ffff47ee256eae60df77155568","first_computed_at":"2026-05-17T23:40:57.301023Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:57.301023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PqfhWNTNd0JcWCE91wxRiaYQlNGs/mQXH2ZxId7t4ch+fJLdNuOHDca9Pw9xLQpZV2C8i1Nl1kpfrOizfUB2BQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:57.301716Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.04632","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a700712616af269cc0210d0497ca06faf2e89e96a3af32018c9e555793c02d4c","sha256:03b7f232531f37ad21e64b43d985832ba3546ea603aa81d0c84a0e364d50e089"],"state_sha256":"aabba3d0c8afced7d9b3cac4f0dec0b746025d52149d7eaa9f3a155021347999"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/zH6MZkYS3Z3pcpRFHaMxZ6mu4NwX00TjDrAMTj5Q+TvMsniZKolGVFSBWbu8MAKUuFKVPj1eygv2V0guRWoAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T18:20:51.894832Z","bundle_sha256":"de949b12b54927680a0fdbddcfb26e5bfa30a94746db921b3d6e53c344ffdab0"}}