{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SFJCTZTBPG4H2XXGDXM4MTXEEX","short_pith_number":"pith:SFJCTZTB","canonical_record":{"source":{"id":"1711.00002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-30T22:01:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a4aaa8b471eff8f61cc93b8667bbeb9da976b98af854785f0694903e93c85f5a","abstract_canon_sha256":"e4b205826bc4a3d5aa239f2ef1c57b36b41338280eceb2c007e4608cd698791a"},"schema_version":"1.0"},"canonical_sha256":"915229e66179b87d5ee61dd9c64ee425c02e11e45be91d0df0fec9cefdc4a5a4","source":{"kind":"arxiv","id":"1711.00002","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00002","created_at":"2026-05-18T00:31:35Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00002v1","created_at":"2026-05-18T00:31:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00002","created_at":"2026-05-18T00:31:35Z"},{"alias_kind":"pith_short_12","alias_value":"SFJCTZTBPG4H","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SFJCTZTBPG4H2XXG","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SFJCTZTB","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SFJCTZTBPG4H2XXGDXM4MTXEEX","target":"record","payload":{"canonical_record":{"source":{"id":"1711.00002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-30T22:01:08Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a4aaa8b471eff8f61cc93b8667bbeb9da976b98af854785f0694903e93c85f5a","abstract_canon_sha256":"e4b205826bc4a3d5aa239f2ef1c57b36b41338280eceb2c007e4608cd698791a"},"schema_version":"1.0"},"canonical_sha256":"915229e66179b87d5ee61dd9c64ee425c02e11e45be91d0df0fec9cefdc4a5a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:35.413427Z","signature_b64":"9LDZT343/g4JuX/ImTZuO46U1Ylo3Oxyi6aDpa/zY5SeEqopdcyIrycjWKFbLhkqlELeY5WiYm8Zj8YiuQ8vCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"915229e66179b87d5ee61dd9c64ee425c02e11e45be91d0df0fec9cefdc4a5a4","last_reissued_at":"2026-05-18T00:31:35.412776Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:35.412776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.00002","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":"2026-05-18T00:31:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cpjzPdNAZJ9UoVbqufBmOmBQzprpzqbdqDhS7QlsK70HnsggCG50JY5/RdxcBjRdDnX9cPgTEze+5/xsSUvVAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T16:27:41.968444Z"},"content_sha256":"4817b0f67c91c972cdaefa06498e0c591a46b6404d83eae9bf9fdd8f7511d280","schema_version":"1.0","event_id":"sha256:4817b0f67c91c972cdaefa06498e0c591a46b6404d83eae9bf9fdd8f7511d280"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SFJCTZTBPG4H2XXGDXM4MTXEEX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Log-DenseNet: How to Sparsify a DenseNet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Allison Del Giorno, Debadeepta Dey, Hanzhang Hu, J. Andrew Bagnell, Martial Hebert","submitted_at":"2017-10-30T22:01:08Z","abstract_excerpt":"Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet's extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive. This work first experimentally shows that one key advantage of skip connections is to have short distance"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00002","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":"2026-05-18T00:31:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3L3rJwwUpiyjN30wENlaHRnqaAnSrl+SCCYxuVieop1Ud23baESE0f6DPi8NN7ceQs6I93AgyuQ/C/WZ9fjpCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T16:27:41.968919Z"},"content_sha256":"12c57a2c4b076b95922d717efb9886603f40048047f568d82aa9a46daf993e23","schema_version":"1.0","event_id":"sha256:12c57a2c4b076b95922d717efb9886603f40048047f568d82aa9a46daf993e23"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX/bundle.json","state_url":"https://pith.science/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX/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":"2026-08-15T16:27:41Z","links":{"resolver":"https://pith.science/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX","bundle":"https://pith.science/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX/bundle.json","state":"https://pith.science/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SFJCTZTBPG4H2XXGDXM4MTXEEX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SFJCTZTBPG4H2XXGDXM4MTXEEX","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":"e4b205826bc4a3d5aa239f2ef1c57b36b41338280eceb2c007e4608cd698791a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-30T22:01:08Z","title_canon_sha256":"a4aaa8b471eff8f61cc93b8667bbeb9da976b98af854785f0694903e93c85f5a"},"schema_version":"1.0","source":{"id":"1711.00002","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00002","created_at":"2026-05-18T00:31:35Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00002v1","created_at":"2026-05-18T00:31:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00002","created_at":"2026-05-18T00:31:35Z"},{"alias_kind":"pith_short_12","alias_value":"SFJCTZTBPG4H","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SFJCTZTBPG4H2XXG","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SFJCTZTB","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:12c57a2c4b076b95922d717efb9886603f40048047f568d82aa9a46daf993e23","target":"graph","created_at":"2026-05-18T00:31:35Z","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":"Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet's extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive. This work first experimentally shows that one key advantage of skip connections is to have short distance","authors_text":"Allison Del Giorno, Debadeepta Dey, Hanzhang Hu, J. Andrew Bagnell, Martial Hebert","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-30T22:01:08Z","title":"Log-DenseNet: How to Sparsify a DenseNet"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00002","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":"sha256:4817b0f67c91c972cdaefa06498e0c591a46b6404d83eae9bf9fdd8f7511d280","target":"record","created_at":"2026-05-18T00:31:35Z","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":"e4b205826bc4a3d5aa239f2ef1c57b36b41338280eceb2c007e4608cd698791a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-30T22:01:08Z","title_canon_sha256":"a4aaa8b471eff8f61cc93b8667bbeb9da976b98af854785f0694903e93c85f5a"},"schema_version":"1.0","source":{"id":"1711.00002","kind":"arxiv","version":1}},"canonical_sha256":"915229e66179b87d5ee61dd9c64ee425c02e11e45be91d0df0fec9cefdc4a5a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"915229e66179b87d5ee61dd9c64ee425c02e11e45be91d0df0fec9cefdc4a5a4","first_computed_at":"2026-05-18T00:31:35.412776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:35.412776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9LDZT343/g4JuX/ImTZuO46U1Ylo3Oxyi6aDpa/zY5SeEqopdcyIrycjWKFbLhkqlELeY5WiYm8Zj8YiuQ8vCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:35.413427Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.00002","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4817b0f67c91c972cdaefa06498e0c591a46b6404d83eae9bf9fdd8f7511d280","sha256:12c57a2c4b076b95922d717efb9886603f40048047f568d82aa9a46daf993e23"],"state_sha256":"8973865a8bb2b02440dbed2cc11df73d9449811ab01c52909dbaad655ef3e5c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"29HQGCqWnFxQ4u6emH9UbaGDaCM44F0fIXqMtsG9OOLKcObMUvU6tmKVBdRbB0/1nn/hxjfgmp3EzJjT7bTjBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T16:27:41.973064Z","bundle_sha256":"3c75953349cfec5409d217ed0d9e5eaa7e873cd148ac922033d34066a0c71475"}}