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Paper Citation Record · LEDGER

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.26346.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.26346 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T00:10:52.644242Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation bb3064c3-0333-49c8-b285-b58b973550e2 · outbound

This paper cites A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning A review of biomedical datasets relating to drug discovery: a knowledge graph perspective

Reference 1

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Observation 4a35cf2d-d99c-4863-8da2-bae51b4f96c8 · outbound

This paper cites Translating embeddings for modeling multi-relational data.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Translating embeddings for modeling multi-relational data

Reference 2

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Observation f61ff222-4723-4818-8645-26c898aa5809 · outbound

This paper cites Building a knowledge graph to enable precision medicine.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Building a knowledge graph to enable precision medicine

Reference 3

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Observation d182d46c-e7ce-4ec6-a60d-ae08e77d09fb · outbound

This paper cites Kernel methods for deep learning.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Kernel methods for deep learning

Reference 4

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Observation 2a7618cb-50af-49b8-b2b1-6fdd8ccf2bbc · outbound

This paper cites Inductive entity representations from text via link prediction.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Inductive entity representations from text via link prediction

Reference 5

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Observation c82c1545-477c-4a62-b70b-84ea0fa6ff6e · outbound

This paper cites The spectrum of kernel random matrices.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning The spectrum of kernel random matrices

Reference 6

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This paper cites Distributed estimation of principal eigenspaces.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Distributed estimation of principal eigenspaces

Reference 7

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Observation 75bcff85-6fad-4d50-8e6f-4cd00f915973 · outbound

This paper cites Remainder formulae in taylor's theorem.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Remainder formulae in taylor's theorem

Reference 8

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Observation a2f29bac-a202-44ab-be38-d03a867b18f1 · outbound

This paper cites On the provable advantage of unsupervised pretraining.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning On the provable advantage of unsupervised pretraining

Reference 9

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Observation ecb8fa4f-79be-4c42-8297-e72b864fe7c7 · outbound

This paper cites HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma

Reference 10

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Observation b7f8f13a-ea79-4f25-a827-f8b086f6d374 · outbound

This paper cites Hoff, Adrian E.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Hoff, Adrian E

Reference 11

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Observation 50fb0090-eb7f-4eed-8bbb-26f826b84e26 · outbound

This paper cites Knowledge graph embedding based question answering.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Knowledge graph embedding based question answering

Reference 12

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Observation ec54f8c1-d436-4f4f-85c7-f7b24b895e9f · outbound

This paper cites A survey on knowledge graphs: Representation, acquisition, and applications.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning A survey on knowledge graphs: Representation, acquisition, and applications

Reference 13

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This paper cites Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Lee, Qi Lei, Nikunj Saunshi, and Jiacheng Zhuo

Reference 14

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This paper cites Representation-Enhanced Neural Knowledge Integration with Application to Large-Scale Medical Ontology Learning.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Representation-Enhanced Neural Knowledge Integration with Application to Large-Scale Medical Ontology Learning

Reference 15

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This paper cites MacDonald, Elizaveta Levina, and Ji Zhu.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning MacDonald, Elizaveta Levina, and Ji Zhu

Reference 16

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This paper cites WordNet : a lexical database for english.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning WordNet : a lexical database for english

Reference 17

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This paper cites A review of relational machine learning for knowledge graphs.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning A review of relational machine learning for knowledge graphs

Reference 18

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This paper cites Invariance principles for homogeneous sums: universality of gaussian wiener chaos.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Invariance principles for homogeneous sums: universality of gaussian wiener chaos

Reference 19

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning A theoretical analysis of contrastive unsupervised representation learning

Reference 20

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This paper cites RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Reference 21

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Complex embeddings for simple link prediction

Reference 22

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This paper cites Structure-augmented text representation learning for efficient knowledge graph completion.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Structure-augmented text representation learning for efficient knowledge graph completion

Reference 23

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning SimKGC : Simple contrastive knowledge graph completion with pre-trained language models

Reference 24

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning KEPLER : A unified model for knowledge embedding and pre-trained language representation

Reference 25

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Deep kernel learning

Reference 26

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Representation learning of knowledge graphs with entity descriptions

Reference 27

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Inference for heteroskedastic pca with missing data

Reference 28

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Reference 29

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning A survey of information extraction based on deep learning

Reference 30

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning KG-BERT: BERT for Knowledge Graph Completion

Reference 31

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Error bounds for approximations with deep ReLU networks

Reference 32

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This paper cites A useful variant of the Davis--Kahan theorem for statisticians.

Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning A useful variant of the Davis--Kahan theorem for statisticians

Reference 33

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Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning Collaborative knowledge base embedding for recommender systems

Reference 34

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