{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:J5RYOZSI43V2YWPWUPTYMFNVNQ","short_pith_number":"pith:J5RYOZSI","canonical_record":{"source":{"id":"1408.6804","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-08-28T18:38:24Z","cross_cats_sorted":[],"title_canon_sha256":"107c4bb95939445bcf33563397a405357c6bb48746208a3032989f5d2d531c74","abstract_canon_sha256":"5b36008970c99c3d987a76678470cffd650d9181099d130ac3e79d55e9f2942b"},"schema_version":"1.0"},"canonical_sha256":"4f63876648e6ebac59f6a3e78615b56c36ec7d261cc017ffea31c42f49c36bc8","source":{"kind":"arxiv","id":"1408.6804","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1408.6804","created_at":"2026-05-18T02:34:51Z"},{"alias_kind":"arxiv_version","alias_value":"1408.6804v2","created_at":"2026-05-18T02:34:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.6804","created_at":"2026-05-18T02:34:51Z"},{"alias_kind":"pith_short_12","alias_value":"J5RYOZSI43V2","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"J5RYOZSI43V2YWPW","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"J5RYOZSI","created_at":"2026-05-18T12:28:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:J5RYOZSI43V2YWPWUPTYMFNVNQ","target":"record","payload":{"canonical_record":{"source":{"id":"1408.6804","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-08-28T18:38:24Z","cross_cats_sorted":[],"title_canon_sha256":"107c4bb95939445bcf33563397a405357c6bb48746208a3032989f5d2d531c74","abstract_canon_sha256":"5b36008970c99c3d987a76678470cffd650d9181099d130ac3e79d55e9f2942b"},"schema_version":"1.0"},"canonical_sha256":"4f63876648e6ebac59f6a3e78615b56c36ec7d261cc017ffea31c42f49c36bc8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:34:51.818912Z","signature_b64":"ismSHiP1B5Ce0ltEt3Is6CjdvOXWxKuGf9L07YZArvNpyWbiImZ2LjGZyViVrHcizxIGejVuiTML69WPU2Z9Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f63876648e6ebac59f6a3e78615b56c36ec7d261cc017ffea31c42f49c36bc8","last_reissued_at":"2026-05-18T02:34:51.818572Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:34:51.818572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1408.6804","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":"2026-05-18T02:34:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WVpre1eCHO6eLucSHuuekJq3F6ZBqZlDl4cGk06D+SSbDQ8gIFJp7Bce9ztBsI2QB1LRlgeblTW6rZxYb9YICA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:56:24.772670Z"},"content_sha256":"c39c8fd7fda3cc4832881c8c12f34cdbe8687f132c2054e9c9ca49a93d2ce7b1","schema_version":"1.0","event_id":"sha256:c39c8fd7fda3cc4832881c8c12f34cdbe8687f132c2054e9c9ca49a93d2ce7b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:J5RYOZSI43V2YWPWUPTYMFNVNQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christoph H. Lampert, Neel Shah, Vladimir Kolmogorov","submitted_at":"2014-08-28T18:38:24Z","abstract_excerpt":"Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \\emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut.\n  In this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.6804","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":"2026-05-18T02:34:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8pcFgWXCm8/4vZj6+ral9r1e1iv9tk3wZi/ls9UU1sPfSWhMdGx8CfaDAbY6146L56lY16Y+zuR4yoK24FBYAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:56:24.773351Z"},"content_sha256":"cb513f83b5658bb11dbd8ac16500dcdc4dde25e622f09acfcf3764774daed667","schema_version":"1.0","event_id":"sha256:cb513f83b5658bb11dbd8ac16500dcdc4dde25e622f09acfcf3764774daed667"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ/bundle.json","state_url":"https://pith.science/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ/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":"2026-06-06T19:56:24Z","links":{"resolver":"https://pith.science/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ","bundle":"https://pith.science/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ/bundle.json","state":"https://pith.science/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J5RYOZSI43V2YWPWUPTYMFNVNQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:J5RYOZSI43V2YWPWUPTYMFNVNQ","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":"5b36008970c99c3d987a76678470cffd650d9181099d130ac3e79d55e9f2942b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-08-28T18:38:24Z","title_canon_sha256":"107c4bb95939445bcf33563397a405357c6bb48746208a3032989f5d2d531c74"},"schema_version":"1.0","source":{"id":"1408.6804","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1408.6804","created_at":"2026-05-18T02:34:51Z"},{"alias_kind":"arxiv_version","alias_value":"1408.6804v2","created_at":"2026-05-18T02:34:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.6804","created_at":"2026-05-18T02:34:51Z"},{"alias_kind":"pith_short_12","alias_value":"J5RYOZSI43V2","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_16","alias_value":"J5RYOZSI43V2YWPW","created_at":"2026-05-18T12:28:33Z"},{"alias_kind":"pith_short_8","alias_value":"J5RYOZSI","created_at":"2026-05-18T12:28:33Z"}],"graph_snapshots":[{"event_id":"sha256:cb513f83b5658bb11dbd8ac16500dcdc4dde25e622f09acfcf3764774daed667","target":"graph","created_at":"2026-05-18T02:34:51Z","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":"Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \\emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut.\n  In this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in comp","authors_text":"Christoph H. Lampert, Neel Shah, Vladimir Kolmogorov","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-08-28T18:38:24Z","title":"A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.6804","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:c39c8fd7fda3cc4832881c8c12f34cdbe8687f132c2054e9c9ca49a93d2ce7b1","target":"record","created_at":"2026-05-18T02:34:51Z","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":"5b36008970c99c3d987a76678470cffd650d9181099d130ac3e79d55e9f2942b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-08-28T18:38:24Z","title_canon_sha256":"107c4bb95939445bcf33563397a405357c6bb48746208a3032989f5d2d531c74"},"schema_version":"1.0","source":{"id":"1408.6804","kind":"arxiv","version":2}},"canonical_sha256":"4f63876648e6ebac59f6a3e78615b56c36ec7d261cc017ffea31c42f49c36bc8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f63876648e6ebac59f6a3e78615b56c36ec7d261cc017ffea31c42f49c36bc8","first_computed_at":"2026-05-18T02:34:51.818572Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:34:51.818572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ismSHiP1B5Ce0ltEt3Is6CjdvOXWxKuGf9L07YZArvNpyWbiImZ2LjGZyViVrHcizxIGejVuiTML69WPU2Z9Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:34:51.818912Z","signed_message":"canonical_sha256_bytes"},"source_id":"1408.6804","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c39c8fd7fda3cc4832881c8c12f34cdbe8687f132c2054e9c9ca49a93d2ce7b1","sha256:cb513f83b5658bb11dbd8ac16500dcdc4dde25e622f09acfcf3764774daed667"],"state_sha256":"5dea4f5bebe061bea9e8eb14021b4a0afe911d4ba3693f956e35bbfc99f5f1d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d0bZ0EYX23CUMffI4iDoyk99BlMRTOghcTSMD24jyagpmerAerrnU8wzIuZxlXDamQMSBPFIO54LppGpGtlDBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T19:56:24.776917Z","bundle_sha256":"03edf8fe97fb5decd2aaef1771d88e97d61bd9b3ca1e6b3f77f9bba889d98504"}}