{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:26C2TU2XDBSO7SYULAAQDOJA76","short_pith_number":"pith:26C2TU2X","canonical_record":{"source":{"id":"1711.09224","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-25T09:34:12Z","cross_cats_sorted":[],"title_canon_sha256":"d8efc5031d0194bd7165f6d1e123698aed66a637382728ac61da039bfe1d8235","abstract_canon_sha256":"b94466441d25e7987fa469cc152601318be4baa563e99a19c843f9206517185b"},"schema_version":"1.0"},"canonical_sha256":"d785a9d3571864efcb14580101b920ffbaa015e736f47e84a38691623474b001","source":{"kind":"arxiv","id":"1711.09224","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.09224","created_at":"2026-05-18T00:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"1711.09224v2","created_at":"2026-05-18T00:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.09224","created_at":"2026-05-18T00:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"26C2TU2XDBSO","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"26C2TU2XDBSO7SYU","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"26C2TU2X","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:26C2TU2XDBSO7SYULAAQDOJA76","target":"record","payload":{"canonical_record":{"source":{"id":"1711.09224","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-25T09:34:12Z","cross_cats_sorted":[],"title_canon_sha256":"d8efc5031d0194bd7165f6d1e123698aed66a637382728ac61da039bfe1d8235","abstract_canon_sha256":"b94466441d25e7987fa469cc152601318be4baa563e99a19c843f9206517185b"},"schema_version":"1.0"},"canonical_sha256":"d785a9d3571864efcb14580101b920ffbaa015e736f47e84a38691623474b001","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:00.624404Z","signature_b64":"4hPTGr1oo6yZgRICz2UMwP5iEPONDY8FNrlMTnkg/itwB8fFni6Zs8JZkrHZ35F0KlYe2vReVAhO5dWoIhGeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d785a9d3571864efcb14580101b920ffbaa015e736f47e84a38691623474b001","last_reissued_at":"2026-05-18T00:14:00.623804Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:00.623804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.09224","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8xCCoisidldcITCSCylyja0A9m/e66Jh7eT5NeOx87ZUHYCyeaA+b1aKr3GUaTeonHWv+8uMbnftS0O17MG4Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T15:27:17.962038Z"},"content_sha256":"dd5c5cb557bd7e7dc432401ee75fa69a7453ccd9652f61b91a7197d92e569082","schema_version":"1.0","event_id":"sha256:dd5c5cb557bd7e7dc432401ee75fa69a7453ccd9652f61b91a7197d92e569082"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:26C2TU2XDBSO7SYULAAQDOJA76","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CondenseNet: An Efficient DenseNet using Learned Group Convolutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gao Huang, Kilian Q. Weinberger, Laurens van der Maaten, Shichen Liu","submitted_at":"2017-11-25T09:34:12Z","abstract_excerpt":"Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.09224","kind":"arxiv","version":2},"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":"2026-05-18T00:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vXdUQA6vWr3nZ4bhQEpg+KLXRUFIvv4VEQ1avypZonJR9Og6pDm4Sqz7FapcCbkcBUwXabEaMhR4Unpr/XWfDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T15:27:17.962797Z"},"content_sha256":"94b1d75ec173a05c7da17c23feb7ea5cafbcfe6ca635e3c83d8fae3b699fe5ac","schema_version":"1.0","event_id":"sha256:94b1d75ec173a05c7da17c23feb7ea5cafbcfe6ca635e3c83d8fae3b699fe5ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/26C2TU2XDBSO7SYULAAQDOJA76/bundle.json","state_url":"https://pith.science/pith/26C2TU2XDBSO7SYULAAQDOJA76/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/26C2TU2XDBSO7SYULAAQDOJA76/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":"2026-05-28T15:27:17Z","links":{"resolver":"https://pith.science/pith/26C2TU2XDBSO7SYULAAQDOJA76","bundle":"https://pith.science/pith/26C2TU2XDBSO7SYULAAQDOJA76/bundle.json","state":"https://pith.science/pith/26C2TU2XDBSO7SYULAAQDOJA76/state.json","well_known_bundle":"https://pith.science/.well-known/pith/26C2TU2XDBSO7SYULAAQDOJA76/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:26C2TU2XDBSO7SYULAAQDOJA76","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":"b94466441d25e7987fa469cc152601318be4baa563e99a19c843f9206517185b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-25T09:34:12Z","title_canon_sha256":"d8efc5031d0194bd7165f6d1e123698aed66a637382728ac61da039bfe1d8235"},"schema_version":"1.0","source":{"id":"1711.09224","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.09224","created_at":"2026-05-18T00:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"1711.09224v2","created_at":"2026-05-18T00:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.09224","created_at":"2026-05-18T00:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"26C2TU2XDBSO","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"26C2TU2XDBSO7SYU","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"26C2TU2X","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:94b1d75ec173a05c7da17c23feb7ea5cafbcfe6ca635e3c83d8fae3b699fe5ac","target":"graph","created_at":"2026-05-18T00:14:00Z","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 are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity with a novel module called learned group convolution. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard group convolutions, allowing for efficient computation in practice. Our e","authors_text":"Gao Huang, Kilian Q. Weinberger, Laurens van der Maaten, Shichen Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-25T09:34:12Z","title":"CondenseNet: An Efficient DenseNet using Learned Group Convolutions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.09224","kind":"arxiv","version":2},"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:dd5c5cb557bd7e7dc432401ee75fa69a7453ccd9652f61b91a7197d92e569082","target":"record","created_at":"2026-05-18T00:14:00Z","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":"b94466441d25e7987fa469cc152601318be4baa563e99a19c843f9206517185b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-11-25T09:34:12Z","title_canon_sha256":"d8efc5031d0194bd7165f6d1e123698aed66a637382728ac61da039bfe1d8235"},"schema_version":"1.0","source":{"id":"1711.09224","kind":"arxiv","version":2}},"canonical_sha256":"d785a9d3571864efcb14580101b920ffbaa015e736f47e84a38691623474b001","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d785a9d3571864efcb14580101b920ffbaa015e736f47e84a38691623474b001","first_computed_at":"2026-05-18T00:14:00.623804Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:00.623804Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4hPTGr1oo6yZgRICz2UMwP5iEPONDY8FNrlMTnkg/itwB8fFni6Zs8JZkrHZ35F0KlYe2vReVAhO5dWoIhGeDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:00.624404Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.09224","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd5c5cb557bd7e7dc432401ee75fa69a7453ccd9652f61b91a7197d92e569082","sha256:94b1d75ec173a05c7da17c23feb7ea5cafbcfe6ca635e3c83d8fae3b699fe5ac"],"state_sha256":"2c0b7bfdc68cb205f652f876f0a04a730ae32fa5db395be15c70705e15733ac5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6hh+Eyj75XkjAZ6ut45ViX//XunZJCZJWCU8IiMXbpmfd9yDHCfTu/2vcLAUv+k1PcKrTvV6R9k6+NaLpzceBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T15:27:17.966297Z","bundle_sha256":"cab103d0a91374c784990ddf4b2ae5a537ba7eb299b6e8d30b2845474a58426c"}}