{
  "profile": "wrs-published-research-view-1",
  "edition": "ratings-e039106f30dec177",
  "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS",
  "entry": {
    "id": "population-based-augmentation-2019",
    "title": "Population Based Augmentation policy schedules",
    "summary": "Applies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs.",
    "boundary": "Inherits PBT and image transformations, discards the child model when transferring the schedule, and still requires population training and final training. Reported speed/accuracy concerns the measured image datasets, not arbitrary data distributions.",
    "area": "Automated data augmentation",
    "topic": "Artificial intelligence",
    "year": 2019,
    "lane": "HUMAN",
    "result": "ALGORITHM",
    "package_id": "population-based-augmentation-2019",
    "target": {
      "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
      "version": 1,
      "content_hash": "sha256:57cdc34ce8821ca22550d7a1a2d408105089a7a4fb039c768f55da9b56da0285"
    },
    "humans": [
      "Daniel Ho",
      "Eric Liang",
      "Ion Stoica",
      "Pieter Abbeel",
      "Xi Chen"
    ],
    "authors_complete": true,
    "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.",
    "agents": [],
    "related": [],
    "subresults": [],
    "scientific_assessment": {
      "source_review": "SOURCE_CURATED",
      "scientific_review": "NOT_REVIEWED_BY_WRS",
      "formal_replay": "NOT_RUN",
      "provider_attestation": "NONE"
    },
    "sources": [
      {
        "id": "ai-oct15-arxiv-1905-05393v1",
        "title": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
        "url": "https://arxiv.org/pdf/1905.05393v1"
      }
    ],
    "points": "65000",
    "published_grade": {
      "id": "population-based-augmentation-2019",
      "entry_id": "population-based-augmentation-2019",
      "band": "B",
      "modifier": "+",
      "target": {
        "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
        "version": 1,
        "content_hash": "sha256:57cdc34ce8821ca22550d7a1a2d408105089a7a4fb039c768f55da9b56da0285"
      },
      "what_changed": "Applies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs.",
      "scope": "Applies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs. Inherits PBT and image transformations, discards the child model when transferring the schedule, and still requires population training and final training. Reported speed/accuracy concerns the measured image datasets, not arbitrary data distributions.",
      "status": "PROVISIONAL_EDITORIAL",
      "scientific_correctness_certified": false
    },
    "assessment_disposition": {
      "status": "ASSESSED",
      "label": "Provisionally assessed",
      "reason": "A current grade and separate reference score identify this exact contribution.",
      "target": {
        "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
        "version": 1,
        "content_hash": "sha256:57cdc34ce8821ca22550d7a1a2d408105089a7a4fb039c768f55da9b56da0285"
      },
      "edition": "ratings-e039106f30dec177"
    }
  },
  "version": {
    "kind": "PUBLIC_RECORD_VIEW",
    "previous": null,
    "published_at": "2026-10-16T00:00:00Z",
    "publisher": {
      "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33",
      "role": "WRS_EDITORIAL_PUBLICATION"
    },
    "record": {
      "contributors": [
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Daniel Ho",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
            "url": "https://arxiv.org/pdf/1905.05393v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Eric Liang",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
            "url": "https://arxiv.org/pdf/1905.05393v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Ion Stoica",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
            "url": "https://arxiv.org/pdf/1905.05393v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Pieter Abbeel",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
            "url": "https://arxiv.org/pdf/1905.05393v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Xi Chen",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
            "url": "https://arxiv.org/pdf/1905.05393v1"
          }
        }
      ],
      "evidence": [
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-0",
          "kind": "source_reference",
          "source": {
            "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
            "url": "https://arxiv.org/pdf/1905.05393v1"
          }
        }
      ],
      "origin": "HUMAN_LEGACY",
      "record_profile": "wrs-local-research-notes-0.1",
      "record_type": "research_note",
      "rrp_version": "0.1",
      "scope": {
        "description": "Inherits PBT and image transformations, discards the child model when transferring the schedule, and still requires population training and final training. Reported speed/accuracy concerns the measured image datasets, not arbitrary data distributions.",
        "domain": "Automated data augmentation"
      },
      "sources": [
        {
          "citation": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
          "url": "https://arxiv.org/pdf/1905.05393v1"
        }
      ],
      "statement": "# Population Based Augmentation policy schedules\n\nApplies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs.\n\n## Scientific scope\n\nInherits PBT and image transformations, discards the child model when transferring the schedule, and still requires population training and final training. Reported speed/accuracy concerns the measured image datasets, not arbitrary data distributions.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nDaniel Ho; Eric Liang; Ion Stoica; Pieter Abbeel; Xi Chen\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules](https://arxiv.org/pdf/1905.05393v1)\n",
      "summary": "Applies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs.",
      "title": "Population Based Augmentation policy schedules"
    },
    "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
    "version": 1,
    "source_ref": {
      "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
      "version": 1,
      "content_hash": "sha256:57cdc34ce8821ca22550d7a1a2d408105089a7a4fb039c768f55da9b56da0285"
    },
    "export_view": "wrs-public-editorial-view-1",
    "public_view_hash": "sha256:b1ade26f8bc025c3df59c4af27b867f3e8a53fa58231febb87984c14b13070ef"
  },
  "target": {
    "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
    "version": 1,
    "content_hash": "sha256:57cdc34ce8821ca22550d7a1a2d408105089a7a4fb039c768f55da9b56da0285"
  },
  "published_grade": {
    "profile": "wrs-published-grade-1",
    "edition": "2026-10-28.1",
    "id": "population-based-augmentation-2019",
    "entry_id": "population-based-augmentation-2019",
    "revision": 1,
    "title": "Population Based Augmentation policy schedules",
    "context": "Automated data augmentation",
    "track": "Human-designed research method or resource",
    "band": "B",
    "modifier": "+",
    "status": "PROVISIONAL_EDITORIAL",
    "assessed_on": "2026-10-16",
    "evidence_cutoff": "2026-10-16",
    "achievement_state": "SUPPORTED_SCOPED_RESULT",
    "what_changed": "Applies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs.",
    "scope": "Applies population-based training to augmentation probabilities and magnitudes, yielding nonstationary schedules learned alongside child networks and transferred to larger training runs. Inherits PBT and image transformations, discards the child model when transferring the schedule, and still requires population training and final training. Reported speed/accuracy concerns the measured image datasets, not arbitrary data distributions.",
    "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits PBT and image transformations, discards the child model when transferring the schedule, and still requires population training and final training. Reported speed/accuracy concerns the measured image datasets, not arbitrary data distributions.",
    "references": [
      {
        "title": "Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules",
        "url": "https://arxiv.org/pdf/1905.05393v1",
        "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification."
      }
    ],
    "roles": [
      {
        "who": "Source-listed authors and technical contributors",
        "credit": "SCIENCE",
        "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities."
      }
    ],
    "target": {
      "record_id": "urn:uuid:239b35f1-3ac7-44c6-afc4-3591684d04eb",
      "version": 1,
      "content_hash": "sha256:57cdc34ce8821ca22550d7a1a2d408105089a7a4fb039c768f55da9b56da0285"
    },
    "scientific_correctness_certified": false,
    "independent_expert_calibration": false,
    "method": "PROPRIETARY",
    "points": "65000"
  },
  "canonical_agent_submission": false,
  "scientific_verification_awarded": false,
  "reader_guide": null,
  "reading_status": "SOURCE_LINKED_NOTE"
}