{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:4Y2WO5RS6NBZSYPNZ4FOWHWV7H","short_pith_number":"pith:4Y2WO5RS","canonical_record":{"source":{"id":"1810.11367","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-22T20:05:42Z","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"title_canon_sha256":"abb89930128ed91d3fa7774e02afd6e5c4df8b6c7a737a1ba02dc04d41c6592a","abstract_canon_sha256":"1c350955d7dfb624a4a789ceb02f0c89330e6b0e2dfcfc57e1e2af3d224314f5"},"schema_version":"1.0"},"canonical_sha256":"e635677632f3439961edcf0aeb1ed5f9ffea55a5b0300f8309922e3e8df82146","source":{"kind":"arxiv","id":"1810.11367","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.11367","created_at":"2026-05-18T00:02:14Z"},{"alias_kind":"arxiv_version","alias_value":"1810.11367v1","created_at":"2026-05-18T00:02:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11367","created_at":"2026-05-18T00:02:14Z"},{"alias_kind":"pith_short_12","alias_value":"4Y2WO5RS6NBZ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4Y2WO5RS6NBZSYPN","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4Y2WO5RS","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:4Y2WO5RS6NBZSYPNZ4FOWHWV7H","target":"record","payload":{"canonical_record":{"source":{"id":"1810.11367","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-22T20:05:42Z","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"title_canon_sha256":"abb89930128ed91d3fa7774e02afd6e5c4df8b6c7a737a1ba02dc04d41c6592a","abstract_canon_sha256":"1c350955d7dfb624a4a789ceb02f0c89330e6b0e2dfcfc57e1e2af3d224314f5"},"schema_version":"1.0"},"canonical_sha256":"e635677632f3439961edcf0aeb1ed5f9ffea55a5b0300f8309922e3e8df82146","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:14.331374Z","signature_b64":"L9sEoLmi8UDd74/26LC5IP8gAtJ/jojupvVfTxcZrMllR8c4H+K+dEdg4c+8lvuvvfobUM2UB+t9JobnKu/sBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e635677632f3439961edcf0aeb1ed5f9ffea55a5b0300f8309922e3e8df82146","last_reissued_at":"2026-05-18T00:02:14.330744Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:14.330744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.11367","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":"2026-05-18T00:02:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k9jpHHw+WikpAgdfvwuIJ90HELNnlYgKYLAKUZJf2bibBlWKVYUpnRUk5t3P2SDOZyLHT+q6vVP8wrZV4xJKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T21:04:05.288871Z"},"content_sha256":"04faea1226f9e3e2cf7916824967b553c4455787786135d9f9604e0c00fd92b1","schema_version":"1.0","event_id":"sha256:04faea1226f9e3e2cf7916824967b553c4455787786135d9f9604e0c00fd92b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:4Y2WO5RS6NBZSYPNZ4FOWHWV7H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LAMVI-2: A Visual Tool for Comparing and Tuning Word Embedding Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Eytan Adar, Joshua Luckson, Xin Rong","submitted_at":"2018-10-22T20:05:42Z","abstract_excerpt":"Tuning machine learning models, particularly deep learning architectures, is a complex process. Automated hyperparameter tuning algorithms often depend on specific optimization metrics. However, in many situations, a developer trades one metric against another: accuracy versus overfitting, precision versus recall, smaller models and accuracy, etc. With deep learning, not only are the model's representations opaque, the model's behavior when parameters \"knobs\" are changed may also be unpredictable. Thus, picking the \"best\" model often requires time-consuming model comparison. In this work, we i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11367","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":"2026-05-18T00:02:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FFdxKCxd9FOjgqH7NNBHpsCPep6a+eDr6I2vbtY1tjWBEQocfJTt/OiF288He0BoVCeoCSdKfc/bH+oJCiokCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T21:04:05.289218Z"},"content_sha256":"975e2781705e5c4eba998049eaad744d9933861ed16305d0d45205236255d066","schema_version":"1.0","event_id":"sha256:975e2781705e5c4eba998049eaad744d9933861ed16305d0d45205236255d066"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H/bundle.json","state_url":"https://pith.science/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H/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":"2026-07-04T21:04:05Z","links":{"resolver":"https://pith.science/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H","bundle":"https://pith.science/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H/bundle.json","state":"https://pith.science/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4Y2WO5RS6NBZSYPNZ4FOWHWV7H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:4Y2WO5RS6NBZSYPNZ4FOWHWV7H","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":"1c350955d7dfb624a4a789ceb02f0c89330e6b0e2dfcfc57e1e2af3d224314f5","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-22T20:05:42Z","title_canon_sha256":"abb89930128ed91d3fa7774e02afd6e5c4df8b6c7a737a1ba02dc04d41c6592a"},"schema_version":"1.0","source":{"id":"1810.11367","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.11367","created_at":"2026-05-18T00:02:14Z"},{"alias_kind":"arxiv_version","alias_value":"1810.11367v1","created_at":"2026-05-18T00:02:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11367","created_at":"2026-05-18T00:02:14Z"},{"alias_kind":"pith_short_12","alias_value":"4Y2WO5RS6NBZ","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4Y2WO5RS6NBZSYPN","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4Y2WO5RS","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:975e2781705e5c4eba998049eaad744d9933861ed16305d0d45205236255d066","target":"graph","created_at":"2026-05-18T00:02:14Z","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":"Tuning machine learning models, particularly deep learning architectures, is a complex process. Automated hyperparameter tuning algorithms often depend on specific optimization metrics. However, in many situations, a developer trades one metric against another: accuracy versus overfitting, precision versus recall, smaller models and accuracy, etc. With deep learning, not only are the model's representations opaque, the model's behavior when parameters \"knobs\" are changed may also be unpredictable. Thus, picking the \"best\" model often requires time-consuming model comparison. In this work, we i","authors_text":"Eytan Adar, Joshua Luckson, Xin Rong","cross_cats":["cs.HC","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-22T20:05:42Z","title":"LAMVI-2: A Visual Tool for Comparing and Tuning Word Embedding Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11367","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":"sha256:04faea1226f9e3e2cf7916824967b553c4455787786135d9f9604e0c00fd92b1","target":"record","created_at":"2026-05-18T00:02:14Z","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":"1c350955d7dfb624a4a789ceb02f0c89330e6b0e2dfcfc57e1e2af3d224314f5","cross_cats_sorted":["cs.HC","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-22T20:05:42Z","title_canon_sha256":"abb89930128ed91d3fa7774e02afd6e5c4df8b6c7a737a1ba02dc04d41c6592a"},"schema_version":"1.0","source":{"id":"1810.11367","kind":"arxiv","version":1}},"canonical_sha256":"e635677632f3439961edcf0aeb1ed5f9ffea55a5b0300f8309922e3e8df82146","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e635677632f3439961edcf0aeb1ed5f9ffea55a5b0300f8309922e3e8df82146","first_computed_at":"2026-05-18T00:02:14.330744Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:14.330744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L9sEoLmi8UDd74/26LC5IP8gAtJ/jojupvVfTxcZrMllR8c4H+K+dEdg4c+8lvuvvfobUM2UB+t9JobnKu/sBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:14.331374Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.11367","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:04faea1226f9e3e2cf7916824967b553c4455787786135d9f9604e0c00fd92b1","sha256:975e2781705e5c4eba998049eaad744d9933861ed16305d0d45205236255d066"],"state_sha256":"daa027a665fd4b45ab4fb31b07077043a87f27f9eca457b962ee75e2e4fdc0f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zZFH354VY5yjHTGE+/4NQhD8pWFVz0DRv26VWMJWRAAt41qoeKo1ZZCf6u8n+K7RNB8LCOGAnNfEjvFgFiM/Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T21:04:05.291288Z","bundle_sha256":"4337525a7622fb248ac711a7fe84acc0f9f73404e34a54411ea763175b08136e"}}