{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:334B7QVXDKCGZE22M6KZ63LPW4","short_pith_number":"pith:334B7QVX","canonical_record":{"source":{"id":"1803.10311","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-27T20:38:05Z","cross_cats_sorted":["cs.DB","cs.HC","stat.ML"],"title_canon_sha256":"294d6418d5485d7c34a3cdcd85460bc646e3a51532df8e7cd0b5e7dba77aa575","abstract_canon_sha256":"6bae84cb610b5c158924d2cbd2da664cd5e29d2f7b3570686e2231e4acc62f65"},"schema_version":"1.0"},"canonical_sha256":"def81fc2b71a846c935a67959f6d6fb730acbcfc21bd3ebd7266f9e5cba9ed23","source":{"kind":"arxiv","id":"1803.10311","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10311","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10311v2","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10311","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"pith_short_12","alias_value":"334B7QVXDKCG","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"334B7QVXDKCGZE22","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"334B7QVX","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:334B7QVXDKCGZE22M6KZ63LPW4","target":"record","payload":{"canonical_record":{"source":{"id":"1803.10311","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-27T20:38:05Z","cross_cats_sorted":["cs.DB","cs.HC","stat.ML"],"title_canon_sha256":"294d6418d5485d7c34a3cdcd85460bc646e3a51532df8e7cd0b5e7dba77aa575","abstract_canon_sha256":"6bae84cb610b5c158924d2cbd2da664cd5e29d2f7b3570686e2231e4acc62f65"},"schema_version":"1.0"},"canonical_sha256":"def81fc2b71a846c935a67959f6d6fb730acbcfc21bd3ebd7266f9e5cba9ed23","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:42.825706Z","signature_b64":"GwInwIfy1DthUf1Ix1I93CGJmg0xR54lJJWqvP8FOwxJ+hRUe+belkQYEVP3liTpvZThWIridl17yk2ggIKIBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"def81fc2b71a846c935a67959f6d6fb730acbcfc21bd3ebd7266f9e5cba9ed23","last_reissued_at":"2026-05-18T00:15:42.825202Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:42.825202Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1803.10311","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":"2026-05-18T00:15:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0KblxfAjmLtAApC5NjzmhcGQK0qzrRA6TVdE5SMW+CsIvYNnwGZ2E3N66zv2sh2ZLXv31/2eVQVR2yg+DG1CAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T16:29:58.146124Z"},"content_sha256":"932fcae4afe05d6819eb8e1f6d583a77dcb47c2a31c2ad6d6afb2b53a1bb2476","schema_version":"1.0","event_id":"sha256:932fcae4afe05d6819eb8e1f6d583a77dcb47c2a31c2ad6d6afb2b53a1bb2476"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:334B7QVXDKCGZE22M6KZ63LPW4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB","cs.HC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aditya Parameswaran, Doris Xin, Litian Ma, Shuchen Song","submitted_at":"2018-03-27T20:38:05Z","abstract_excerpt":"Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems. To this end, we conduct a small-scale survey of the applied machine learning literature from five distinct application domains. We collect and distill statistics on the role of iter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10311","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":"2026-05-18T00:15:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qgL2+eKjgdqiOvvkGBH927vKn+8vUU6y6wM9oh72AIc8FuJZlk515Pdq7tn1FqIV+/3nu1JCSlIkyd9Kr99wBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T16:29:58.146495Z"},"content_sha256":"5d5a4b781e0e5c1987da8076d862611f340d78e002dcea4bfa91b09e81dcdc12","schema_version":"1.0","event_id":"sha256:5d5a4b781e0e5c1987da8076d862611f340d78e002dcea4bfa91b09e81dcdc12"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/334B7QVXDKCGZE22M6KZ63LPW4/bundle.json","state_url":"https://pith.science/pith/334B7QVXDKCGZE22M6KZ63LPW4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/334B7QVXDKCGZE22M6KZ63LPW4/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":"2026-05-19T16:29:58Z","links":{"resolver":"https://pith.science/pith/334B7QVXDKCGZE22M6KZ63LPW4","bundle":"https://pith.science/pith/334B7QVXDKCGZE22M6KZ63LPW4/bundle.json","state":"https://pith.science/pith/334B7QVXDKCGZE22M6KZ63LPW4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/334B7QVXDKCGZE22M6KZ63LPW4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:334B7QVXDKCGZE22M6KZ63LPW4","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":"6bae84cb610b5c158924d2cbd2da664cd5e29d2f7b3570686e2231e4acc62f65","cross_cats_sorted":["cs.DB","cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-27T20:38:05Z","title_canon_sha256":"294d6418d5485d7c34a3cdcd85460bc646e3a51532df8e7cd0b5e7dba77aa575"},"schema_version":"1.0","source":{"id":"1803.10311","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1803.10311","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"arxiv_version","alias_value":"1803.10311v2","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.10311","created_at":"2026-05-18T00:15:42Z"},{"alias_kind":"pith_short_12","alias_value":"334B7QVXDKCG","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"334B7QVXDKCGZE22","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"334B7QVX","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:5d5a4b781e0e5c1987da8076d862611f340d78e002dcea4bfa91b09e81dcdc12","target":"graph","created_at":"2026-05-18T00:15:42Z","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":"Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems. To this end, we conduct a small-scale survey of the applied machine learning literature from five distinct application domains. We collect and distill statistics on the role of iter","authors_text":"Aditya Parameswaran, Doris Xin, Litian Ma, Shuchen Song","cross_cats":["cs.DB","cs.HC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-27T20:38:05Z","title":"How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.10311","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":"sha256:932fcae4afe05d6819eb8e1f6d583a77dcb47c2a31c2ad6d6afb2b53a1bb2476","target":"record","created_at":"2026-05-18T00:15:42Z","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":"6bae84cb610b5c158924d2cbd2da664cd5e29d2f7b3570686e2231e4acc62f65","cross_cats_sorted":["cs.DB","cs.HC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-27T20:38:05Z","title_canon_sha256":"294d6418d5485d7c34a3cdcd85460bc646e3a51532df8e7cd0b5e7dba77aa575"},"schema_version":"1.0","source":{"id":"1803.10311","kind":"arxiv","version":2}},"canonical_sha256":"def81fc2b71a846c935a67959f6d6fb730acbcfc21bd3ebd7266f9e5cba9ed23","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"def81fc2b71a846c935a67959f6d6fb730acbcfc21bd3ebd7266f9e5cba9ed23","first_computed_at":"2026-05-18T00:15:42.825202Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:42.825202Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GwInwIfy1DthUf1Ix1I93CGJmg0xR54lJJWqvP8FOwxJ+hRUe+belkQYEVP3liTpvZThWIridl17yk2ggIKIBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:42.825706Z","signed_message":"canonical_sha256_bytes"},"source_id":"1803.10311","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:932fcae4afe05d6819eb8e1f6d583a77dcb47c2a31c2ad6d6afb2b53a1bb2476","sha256:5d5a4b781e0e5c1987da8076d862611f340d78e002dcea4bfa91b09e81dcdc12"],"state_sha256":"9e247473693c8ddbc6f92443612448f2fce6e7a0e3b831d2dacb7f67fd7e1671"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zIfBXt6jTq5z+gsZuHzrr4NrJYjWZidvKZtFvOCWPRNwIiJqyEqIbB4KXrPyERZNeZde+prp1FvuM/T7WdnyCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T16:29:58.148482Z","bundle_sha256":"0439c6920a2a3be9a8a36d44c8222c47d4b5346ac77f071cf99e071aa1ab95b0"}}