{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:GXBGUQUBYK6MVDN67LKIC2GNKW","short_pith_number":"pith:GXBGUQUB","canonical_record":{"source":{"id":"1607.07804","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-03T16:34:24Z","cross_cats_sorted":[],"title_canon_sha256":"4362a3663cb4ed9cb47834ea41658582a3663c44673f9d8cd8267c6c07c310c9","abstract_canon_sha256":"c342f78af8d0c0f62c91544b4b6a4af4b9c92eef1879bd742cc9ca92db128f29"},"schema_version":"1.0"},"canonical_sha256":"35c26a4281c2bcca8dbefad48168cd5594f373bd1770c9f2407db558a7d5f83d","source":{"kind":"arxiv","id":"1607.07804","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.07804","created_at":"2026-05-18T01:10:25Z"},{"alias_kind":"arxiv_version","alias_value":"1607.07804v1","created_at":"2026-05-18T01:10:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.07804","created_at":"2026-05-18T01:10:25Z"},{"alias_kind":"pith_short_12","alias_value":"GXBGUQUBYK6M","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"GXBGUQUBYK6MVDN6","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"GXBGUQUB","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:GXBGUQUBYK6MVDN67LKIC2GNKW","target":"record","payload":{"canonical_record":{"source":{"id":"1607.07804","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-03T16:34:24Z","cross_cats_sorted":[],"title_canon_sha256":"4362a3663cb4ed9cb47834ea41658582a3663c44673f9d8cd8267c6c07c310c9","abstract_canon_sha256":"c342f78af8d0c0f62c91544b4b6a4af4b9c92eef1879bd742cc9ca92db128f29"},"schema_version":"1.0"},"canonical_sha256":"35c26a4281c2bcca8dbefad48168cd5594f373bd1770c9f2407db558a7d5f83d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:25.142231Z","signature_b64":"OQxQGUyqU6IShvUNj8W+TUj9U821SOiPw7t1dmXBMYKThvHI3lNHK2/yEpTaBMAHl30QN8XUbaNbH0GuTNPtDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35c26a4281c2bcca8dbefad48168cd5594f373bd1770c9f2407db558a7d5f83d","last_reissued_at":"2026-05-18T01:10:25.141632Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:25.141632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.07804","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":"2026-05-18T01:10:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EUthe9/bqT8u873FuoyOx0JEO+1d5ZKu0jPKQuafBq9ZrgIsZUpBXkhS/yqIrfrc2NonKdBUPa4l4QMd2tPqCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T18:45:09.389104Z"},"content_sha256":"8f6be9dcf6ce511b0a4f0e7953ca8f23d7829ebfeaff9898a1a2db00ac4725fa","schema_version":"1.0","event_id":"sha256:8f6be9dcf6ce511b0a4f0e7953ca8f23d7829ebfeaff9898a1a2db00ac4725fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:GXBGUQUBYK6MVDN67LKIC2GNKW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Naresh Shanbhag, Sai Zhang","submitted_at":"2016-07-03T16:34:24Z","abstract_excerpt":"In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architectural-level error models in a commercial 45 nm C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.07804","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":"2026-05-18T01:10:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WYBvq+tSL+ZtDhkeHaq9rcoe5ED03+rNgjnlAelVsuDhY6ZdX3rIeqsVMBPIK+La20xs+EU1ztxANaynKQpkBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T18:45:09.389454Z"},"content_sha256":"bd0161beb1d5989a61959ce22c18aec993f959d7dfb8645425e2743cf968ba3e","schema_version":"1.0","event_id":"sha256:bd0161beb1d5989a61959ce22c18aec993f959d7dfb8645425e2743cf968ba3e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GXBGUQUBYK6MVDN67LKIC2GNKW/bundle.json","state_url":"https://pith.science/pith/GXBGUQUBYK6MVDN67LKIC2GNKW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GXBGUQUBYK6MVDN67LKIC2GNKW/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":"2026-06-02T18:45:09Z","links":{"resolver":"https://pith.science/pith/GXBGUQUBYK6MVDN67LKIC2GNKW","bundle":"https://pith.science/pith/GXBGUQUBYK6MVDN67LKIC2GNKW/bundle.json","state":"https://pith.science/pith/GXBGUQUBYK6MVDN67LKIC2GNKW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GXBGUQUBYK6MVDN67LKIC2GNKW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:GXBGUQUBYK6MVDN67LKIC2GNKW","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":"c342f78af8d0c0f62c91544b4b6a4af4b9c92eef1879bd742cc9ca92db128f29","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-03T16:34:24Z","title_canon_sha256":"4362a3663cb4ed9cb47834ea41658582a3663c44673f9d8cd8267c6c07c310c9"},"schema_version":"1.0","source":{"id":"1607.07804","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.07804","created_at":"2026-05-18T01:10:25Z"},{"alias_kind":"arxiv_version","alias_value":"1607.07804v1","created_at":"2026-05-18T01:10:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.07804","created_at":"2026-05-18T01:10:25Z"},{"alias_kind":"pith_short_12","alias_value":"GXBGUQUBYK6M","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"GXBGUQUBYK6MVDN6","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"GXBGUQUB","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:bd0161beb1d5989a61959ce22c18aec993f959d7dfb8645425e2743cf968ba3e","target":"graph","created_at":"2026-05-18T01:10:25Z","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":"In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architectural-level error models in a commercial 45 nm C","authors_text":"Naresh Shanbhag, Sai Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-03T16:34:24Z","title":"Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.07804","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":"sha256:8f6be9dcf6ce511b0a4f0e7953ca8f23d7829ebfeaff9898a1a2db00ac4725fa","target":"record","created_at":"2026-05-18T01:10:25Z","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":"c342f78af8d0c0f62c91544b4b6a4af4b9c92eef1879bd742cc9ca92db128f29","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-03T16:34:24Z","title_canon_sha256":"4362a3663cb4ed9cb47834ea41658582a3663c44673f9d8cd8267c6c07c310c9"},"schema_version":"1.0","source":{"id":"1607.07804","kind":"arxiv","version":1}},"canonical_sha256":"35c26a4281c2bcca8dbefad48168cd5594f373bd1770c9f2407db558a7d5f83d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35c26a4281c2bcca8dbefad48168cd5594f373bd1770c9f2407db558a7d5f83d","first_computed_at":"2026-05-18T01:10:25.141632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:10:25.141632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OQxQGUyqU6IShvUNj8W+TUj9U821SOiPw7t1dmXBMYKThvHI3lNHK2/yEpTaBMAHl30QN8XUbaNbH0GuTNPtDw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:10:25.142231Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.07804","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8f6be9dcf6ce511b0a4f0e7953ca8f23d7829ebfeaff9898a1a2db00ac4725fa","sha256:bd0161beb1d5989a61959ce22c18aec993f959d7dfb8645425e2743cf968ba3e"],"state_sha256":"0217e8fd2ecb4211550b4d9ec926fdaf0a3a540f46f3d0128d64c4a194e63096"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LlqJfvUCxWCCTaUyEVWXXo+UWQRS6cSSAascUz9eD6z2XqlBuwPSsnuiwQAlj9d9o/E7pXv+45SBdIXjTqr5DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T18:45:09.391447Z","bundle_sha256":"cd5cf7c7314134573b07e91919d678b6eba0142aea5d7654feaf14b8ad25a344"}}