{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:QERPAPTY2P3G7FNTEJMPJKKCOS","short_pith_number":"pith:QERPAPTY","canonical_record":{"source":{"id":"1807.02609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-07T03:55:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6b2bfddea3355b71fe8d98921057d871490255641256d218c6887936a4b8042e","abstract_canon_sha256":"80161ff9a5c4c5796b864a0a407e5559e47fc35ba7eff25a62a338618154a1c5"},"schema_version":"1.0"},"canonical_sha256":"8122f03e78d3f66f95b32258f4a942748aacb4b6e2bb0e6bd29247b2118bc397","source":{"kind":"arxiv","id":"1807.02609","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02609","created_at":"2026-05-18T00:11:16Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02609v1","created_at":"2026-05-18T00:11:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02609","created_at":"2026-05-18T00:11:16Z"},{"alias_kind":"pith_short_12","alias_value":"QERPAPTY2P3G","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QERPAPTY2P3G7FNT","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QERPAPTY","created_at":"2026-05-18T12:32:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:QERPAPTY2P3G7FNTEJMPJKKCOS","target":"record","payload":{"canonical_record":{"source":{"id":"1807.02609","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-07T03:55:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6b2bfddea3355b71fe8d98921057d871490255641256d218c6887936a4b8042e","abstract_canon_sha256":"80161ff9a5c4c5796b864a0a407e5559e47fc35ba7eff25a62a338618154a1c5"},"schema_version":"1.0"},"canonical_sha256":"8122f03e78d3f66f95b32258f4a942748aacb4b6e2bb0e6bd29247b2118bc397","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:16.564347Z","signature_b64":"TQhwPxbL/ds5SyjFl4XmEMlu1+P8Hn+Qgvk1bK1NnFrkni5Hsy5JuluSYzyClnKpn0lOMkEjf4JDdws0s2oxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8122f03e78d3f66f95b32258f4a942748aacb4b6e2bb0e6bd29247b2118bc397","last_reissued_at":"2026-05-18T00:11:16.563651Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:16.563651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.02609","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":"2026-05-18T00:11:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QHt3EzBBlc94dXUB9ogxw81kIzJ6BJxTTt0fS9qD01fT/LIrNBHTdTzlsE7I9gaX1ZS4n5Ng9HQvP1IE+593Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T00:51:31.926829Z"},"content_sha256":"73006f9557d785a6ce154948328e27f76978a6c50bc1fd536aecd60378a466d1","schema_version":"1.0","event_id":"sha256:73006f9557d785a6ce154948328e27f76978a6c50bc1fd536aecd60378a466d1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:QERPAPTY2P3G7FNTEJMPJKKCOS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anytime Neural Prediction via Slicing Networks Vertically","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hankook Lee, Jinwoo Shin","submitted_at":"2018-07-07T03:55:26Z","abstract_excerpt":"The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often too heavy to deploy in practice, and it is often required to control their architectures dynamically given computing resource budget, i.e., anytime prediction. While most existing approaches have focused on training multiple shallow sub-networks jointly, we study training thin sub-networks instead. To this end, we first build many inclusive thin sub-networks (of the same depth) under a minor modification of existing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02609","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":"2026-05-18T00:11:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"807VD08LNF0qqMQhlQi65E3plJUuBd43MKUfIQJhMUCV8OS9DHP6NP/G5FsUqndQUmQ19cylMI3mRKTo8Hl1CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T00:51:31.927184Z"},"content_sha256":"934efe5952a3068fbd6817d5f38c13c8df0866141bab26f0523a589e7bb9be47","schema_version":"1.0","event_id":"sha256:934efe5952a3068fbd6817d5f38c13c8df0866141bab26f0523a589e7bb9be47"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QERPAPTY2P3G7FNTEJMPJKKCOS/bundle.json","state_url":"https://pith.science/pith/QERPAPTY2P3G7FNTEJMPJKKCOS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QERPAPTY2P3G7FNTEJMPJKKCOS/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":"2026-05-28T00:51:31Z","links":{"resolver":"https://pith.science/pith/QERPAPTY2P3G7FNTEJMPJKKCOS","bundle":"https://pith.science/pith/QERPAPTY2P3G7FNTEJMPJKKCOS/bundle.json","state":"https://pith.science/pith/QERPAPTY2P3G7FNTEJMPJKKCOS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QERPAPTY2P3G7FNTEJMPJKKCOS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:QERPAPTY2P3G7FNTEJMPJKKCOS","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":"80161ff9a5c4c5796b864a0a407e5559e47fc35ba7eff25a62a338618154a1c5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-07T03:55:26Z","title_canon_sha256":"6b2bfddea3355b71fe8d98921057d871490255641256d218c6887936a4b8042e"},"schema_version":"1.0","source":{"id":"1807.02609","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02609","created_at":"2026-05-18T00:11:16Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02609v1","created_at":"2026-05-18T00:11:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02609","created_at":"2026-05-18T00:11:16Z"},{"alias_kind":"pith_short_12","alias_value":"QERPAPTY2P3G","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_16","alias_value":"QERPAPTY2P3G7FNT","created_at":"2026-05-18T12:32:46Z"},{"alias_kind":"pith_short_8","alias_value":"QERPAPTY","created_at":"2026-05-18T12:32:46Z"}],"graph_snapshots":[{"event_id":"sha256:934efe5952a3068fbd6817d5f38c13c8df0866141bab26f0523a589e7bb9be47","target":"graph","created_at":"2026-05-18T00:11:16Z","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":"The pioneer deep neural networks (DNNs) have emerged to be deeper or wider for improving their accuracy in various applications of artificial intelligence. However, DNNs are often too heavy to deploy in practice, and it is often required to control their architectures dynamically given computing resource budget, i.e., anytime prediction. While most existing approaches have focused on training multiple shallow sub-networks jointly, we study training thin sub-networks instead. To this end, we first build many inclusive thin sub-networks (of the same depth) under a minor modification of existing ","authors_text":"Hankook Lee, Jinwoo Shin","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-07T03:55:26Z","title":"Anytime Neural Prediction via Slicing Networks Vertically"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02609","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":"sha256:73006f9557d785a6ce154948328e27f76978a6c50bc1fd536aecd60378a466d1","target":"record","created_at":"2026-05-18T00:11:16Z","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":"80161ff9a5c4c5796b864a0a407e5559e47fc35ba7eff25a62a338618154a1c5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-07T03:55:26Z","title_canon_sha256":"6b2bfddea3355b71fe8d98921057d871490255641256d218c6887936a4b8042e"},"schema_version":"1.0","source":{"id":"1807.02609","kind":"arxiv","version":1}},"canonical_sha256":"8122f03e78d3f66f95b32258f4a942748aacb4b6e2bb0e6bd29247b2118bc397","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8122f03e78d3f66f95b32258f4a942748aacb4b6e2bb0e6bd29247b2118bc397","first_computed_at":"2026-05-18T00:11:16.563651Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:16.563651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TQhwPxbL/ds5SyjFl4XmEMlu1+P8Hn+Qgvk1bK1NnFrkni5Hsy5JuluSYzyClnKpn0lOMkEjf4JDdws0s2oxAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:16.564347Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.02609","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:73006f9557d785a6ce154948328e27f76978a6c50bc1fd536aecd60378a466d1","sha256:934efe5952a3068fbd6817d5f38c13c8df0866141bab26f0523a589e7bb9be47"],"state_sha256":"fb02affe1c8f1b999c3873de2eadebd07bda6aa47f50f877b864577a2269ef43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dKdh8hE6zNbMIXsVxPYq7ICLifgQvQhDvGzCpj0zAoUlM8LenJldvtGW1yXyYn+4kmSgphrmy481T1dguLNNBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T00:51:31.929198Z","bundle_sha256":"71128ad5a98e0a9c9041eadd9e9a3d77f88d2f6b4ab6bfa549bbb0a1e52a6fae"}}