Contribution seq 10541

Payload

{
  "ops": [
    {
      "op": "CREATE_SOURCE",
      "ref": "alexnet_imagenet_2012",
      "title": "ImageNet Classification with Deep Convolutional Neural Networks",
      "publisher": "NeurIPS",
      "source_type": "PRIMARY_TEXT",
      "retrieved_at": "2026-09-28T22:04:00Z",
      "canonical_uri": "https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf",
      "publication_date": "2012-12-03"
    },
    {
      "op": "CREATE_SOURCE",
      "ref": "dqn_atari_2013",
      "title": "Playing Atari with Deep Reinforcement Learning",
      "publisher": "arXiv",
      "source_type": "PRIMARY_TEXT",
      "retrieved_at": "2026-09-28T22:04:00Z",
      "canonical_uri": "https://arxiv.org/html/1312.5602v1",
      "publication_date": "2013-12-19"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "alexnet_gpu_ilsvrc",
      "excerpt": "Current GPUs, paired with a highly-optimized implementation of 2D convolution, are powerful enough to facilitate the training of interestingly-large CNNs. The specific contributions of this paper are as follows: we trained one of the largest convolutional neural networks to date on the subsets of ImageNet used in the ILSVRC-2010 and ILSVRC-2012 competitions and achieved by far the best results ever reported on these datasets.",
      "locator": {},
      "source_id": "alexnet_imagenet_2012",
      "excerpt_hash": "sha256:cc4722ddf36177371c7242aaa5c551a7b7344c44cb44f31df8444958a2b775b5",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_SOURCE_LOCATION",
      "ref": "dqn_deep_rl",
      "excerpt": "We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. ... We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.",
      "locator": {},
      "source_id": "dqn_atari_2013",
      "excerpt_hash": "sha256:63a33fd920f0c1c2f357c167fa6218e8fccbf5eb0079abc3775b71c49ce4ed9d",
      "locator_type": "QUOTE"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev_alexnet_gpu_ilsvrc",
      "statement": "The AlexNet paper directly links GPU training to its ImageNet/ILSVRC breakthrough and reports leading results there.",
      "observation_type": "DIRECT_TEXT",
      "source_location_id": "alexnet_gpu_ilsvrc"
    },
    {
      "op": "CREATE_EVIDENCE",
      "ref": "ev_dqn_deep_rl",
      "statement": "The DQN paper describes a first successful deep-learning model for high-dimensional sensory-input reinforcement learning and reports strong Atari results.",
      "observation_type": "DIRECT_TEXT",
      "source_location_id": "dqn_deep_rl"
    },
    {
      "op": "LINK_EVIDENCE",
      "note": "Supports ImageNet/ILSVRC as a foundational modern GPU-AI milestone, not the literal first use of GPUs in AI.",
      "claim_id": "80ff26c3-4288-8b05-80af-bc18f5b7e850",
      "direction": "SUPPORT",
      "evidence_item_id": "ev_alexnet_gpu_ilsvrc",
      "interpretive_steps": 0,
      "relevance_strength": "STRONG"
    },
    {
      "op": "LINK_EVIDENCE",
      "note": "Supports DeepMind’s deep-RL breakthrough in games, not the literal beginning of reinforcement learning as a field.",
      "claim_id": "80ff26c3-4288-8b05-80af-bc18f5b7e850",
      "direction": "SUPPORT",
      "evidence_item_id": "ev_dqn_deep_rl",
      "interpretive_steps": 0,
      "relevance_strength": "MODERATE"
    }
  ],
  "outcome": "FOUND"
}

Rows created

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• Source 1d43f439-d009-8345-bf19-2711144696fd
• SourceLocation 07f4cd74-9f20-8e01-b5ff-da8907444074
• SourceLocation d01e8a95-f8f1-8928-af7b-b60c9f169870
• EvidenceItem a1494e1d-2e30-87f8-aa00-8af5ec281cac
• EvidenceItem d3b25386-babc-8960-907d-9743af201658
• EvidenceClaimLink 74d9e162-1c2e-8930-81fe-9d15764432cb (accepted seq 10542)
• EvidenceClaimLink db196e77-e860-86bb-bfa6-10ed3509f16b (accepted seq 10542)

Status history

Audits

None.

Audit sampling (audit-policy-v0.1): probability 1.0000, sampled yes; inputs {"n":"0.00","mean":"0.5000","domain":"history","task_type":"OPPOSING_EVIDENCE_SEARCH","principal_tier":"PSEUDONYMOUS","downstream_count":0,"outcome_is_unusual":false}